Methods for diagnosing lymphoma types

ABSTRACT

Gene expression data provides a basis for more accurate identification and diagnosis of lymphoproliferative disorders. In addition, gene expression data can be used to develop more accurate predictors of survival. The present invention discloses methods for identifying, diagnosing, and predicting survival in a lymphoma or lymphoproliferative disorder on the basis of gene expression patterns. The invention discloses a novel microarray, the Lymph Dx microarray, for obtaining gene expression data from a lymphoma sample. The invention also discloses a variety of methods for utilizing lymphoma gene expression data to determine the identity of a particular lymphoma and to predict survival in a subject diagnosed with a particular lymphoma. This information will be useful in developing the therapeutic approach to be used with a particular subject.

RELATED APPLICATIONS

The present utility application claims priority to provisional patent application U.S. Ser. No. 60/500,377 (Staudt et al.), filed Sep. 3, 2003, the disclosure of which is incorporated by reference herein in its entirety, including but not limited to the electronic data submitted on 21 CD-ROMs accompanying the provisional application.

FIELD OF THE INVENTION

The present invention relates to the field of diagnosing, identifying, and predicting survival in lymphoproliferative disorders.

REFERENCE TO TABLES SUBMITTED ON COMPACT DISC

Tables 2-1723 and 1725-2358 are contained on 21 CD-ROMs provided herewith. These CD-ROMs are numbered 1-21 of 22. Each CD-ROM is provided in two copies, 15 for a total of 44 CD-ROMs. The name, size, and date of creation for each file is presented in the List of Materials Submitted by Compact Disc at the end of this specification and in the file entitled “Tableofcontents.txt,” located on CD number 21 of 22. The name of each file incorporates the number of the corresponding table. Any reference to a table or file should be considered an incorporation by reference of the contents of the table and/or file at that particular place in the specification.

REFERENCE TO COMPUTER PROGRAM LISTING APPENDIX SUBMITTED ON COMPACT DISC

A computer program listing appendix is contained on one CD-ROM provided herewith. Three copies of this CD-ROM, numbered 22 of 22, are provided. The computer program listing appendix contains files related to the implementation of an algorithm for determining lymphoma type. The name, size, and date of creation for each file in the computer program listing appendix is presented in the file entitled “Table_of_contents.txt,” located on CD-ROM 22. Any reference to a file contained in the computer program listing appendix should be considered an incorporation by reference of the contents of that file at that particular place in the specification.

BACKGROUND OF INVENTION

A variety of systems for identifying and classifying lymphomas have been proposed over the last 20 years. In the 1980's, the Working Formulation was introduced as a method of classifying lymphomas based on morphological and clinical characteristics. In the 1990's, the Revised European-American Lymphoma (REAL) system was introduced in an attempt to take into account immunophenotypic and genetic characteristics in classifying lymphomas (Harris 1994). The most recent standard, set forth by the World Health Organization (WHO), attempts to build on these previous systems (Jaffe 2001). The WHO classification of lymphomas is based on several factors, including tumor morphology, immunophenotype, recurrent genetic abnormalities, and clinical features. Table 1, below, contains a list of the B and T cell neoplasms that have been recognized by the WHO classification. Each malignancy is listed according to its WHO classification nomenclature, followed by a WHO classification number.

TABLE 1 Category Name WHO ID # B-cell neoplasms Precursor B-cell Precursor B-cell lymphoblastic 9835/3 neoplasms leukemia Precursor B-cell lymphoblastic 9728/3 lymphoma Mature B-cell Chronic lymphocytic leukemia 9823/3 neoplasms Small lymphocytic lymphoma 9670/3 B-cell prolymphocytic leukemia 9833/3 Lymphoplasmacytic lymphoma 9671/3 Splenic marginal zone 9689/3 lymphoma Hairy cell leukemia 9940/3 Plasma cell myeloma 9732/3 Solitary plasmacytoma of bone 9731/3 Extraosseous plasmacytoma 9734/3 Extranodal marginal zone B-cell 9699/3 lymphoma of mucosa- associated lymphoid tissue (MALT lymphoma) Nodal marginal zone B-cell 9699/3 lymphoma Follicular lymphoma (Grade 1, 9690/3 2, 3a, 3b) Mantle cell lymphoma 9673/3 Diffuse large B-cell lymphoma 9680/3 Mediastinal (thymic) large B-cell 9679/3 lymphoma Intravascular large B-cell 9680/3 lymphoma Primary effusion lymphoma 9678/3 Burkitt lymphoma 9687/3 Burkitt leukemia 9826/3 B-cell proliferations Lymphomatoid granulomatosis 9766/1 of uncertain malignant potential Post-transplant 9970/1 lymphoproliferative disorder, polymorphic T-cell and NK-cell neoplasms Precursor T-cell and Precursor T lymphoblastic 9837/3 NK-cell neoplasms leukemia Precursor T lymphoblastic 9729/3 lymphoma Blastic NK-cell lymphoma 9727/3 Mature T-cell and T-cell prolymphocytic leukemia 9834/3 NK-cell neoplasms T-cell large granular 9831/3 lymphocytic leukemia Aggressive NK-cell leukemia 9948/3 Adult T-cell leukemia/lymphoma 9827/3 Extranodal NK-/T-cell 9719/3 lymphoma, nasal type Enteropathy-type T-cell 9717/3 lymphoma Hepatosplenic T-cell lymphoma 9716/3 Subcutaneous panniculitis-like 9708/3 T-cell lymphoma Mycosis fungoides 9700/3 Sezary syndrome (9701/3) 9701/3 Primary cutaneous anaplastic 9718/3 large cell lymphoma (C-ALCL) Peripheral T-cell lymphoma, 9702/3 unspecified Angioimmunoblastic T-cell 9705/3 lymphoma Anaplastic large cell lymphoma 9714/3 T-cell proliferation Lymphomatoid papulosis 9718/3 of uncertain malignant potential Hodgkin lymphoma Nodular lymphocyte 9659/3 predominant Hodgkin lymphoma Classical Hodgkin lymphoma 9650/3 Classical Hodgkin lymphoma, 9663/3 nodular sclerosis Classical Hodgkin lymphoma, 9651/3 lymphocyte-rich Classical Hodgkin lymphoma, 9652/3 mixed cellularity Classical Hodgkin lymphoma, 9653/3 lymphocyte depleted Other diagnoses that have not been given WHO diagnostic numbers include HIV-associated lymphoma, germinal center B cell-like subtype of diffuse large B cell lymphoma, activated B cell-like subtype of diffuse large B-cell lymphoma, follicular hyperplasia (non-malignant), and infectious mononucleosis (non-malignant).

Although the WHO classification has proven useful in patient management and treatment, patients assigned to the same WHO diagnostic category often have noticeably different clinical outcomes. In many cases, these different outcomes appear to be due to molecular differences between tumors that cannot be readily observed by analyzing tumor morphology. More precise methods are needed for identifying and classifying lymphomas based on their molecular characteristics.

SUMMARY OF THE INVENTION

Accurate identification of lymphoma type or subtype in a subject suffering from a lymphoproliferative disorder is important for developing an appropriate therapeutic strategy. Previous attempts have been made to identify lymphomas using gene expression data obtained using a microarray. However, there is a need in the art for more accurate and predictive methods of analyzing this gene expression data. In addition, there is a need for more specific and efficient methods of obtaining gene expression data.

The present invention discloses a novel microarray for obtaining gene expression data to be used in identifying lymphoma types and predicting survival in a subject. The present invention further discloses a variety of methods for analyzing gene expression data obtained from a lymphoma sample, and specific algorithms for predicting survival and clinical outcome in a subject suffering from a lymphoma.

One embodiment of the present invention provides a composition comprising the set of probes listed in Table 2, contained in the file entitled “Table_(—)0002_LymphDx_Probe_List.txt.” Preferably, this composition comprises a microarray.

In another embodiment, the present invention provides a method of generating a survival predictor for a particular lymphoma type. In this method, one or more biopsy samples that have been diagnosed as belonging to a particular lymphoma type are obtained. Gene expression data is obtained for these samples, and genes with expression patterns associated with longer or shorter survival are identified. Hierarchical clustering is performed to group these genes into gene expression signatures, and the expression of all genes within each signature are averaged to obtain a gene expression signature value for each signature. These gene expression signature values are then used to generate a multivariate survival predictor.

In another embodiment, the present invention provides a method for predicting survival in a follicular lymphoma (FL) subject. In this method, a biopsy sample is obtained from the subject and gene expression data is obtained from the biopsy sample. The expression level of those genes belonging to an immune response-1 or immune response-2 gene expression signature are averaged to generate gene expression signature values for each signature. A survival predictor score is then calculated using an equation: [2.71*(immune response-2 gene expression signature value)]−[2.36*(immune response-1 gene expression signature value)]. A higher survival predictor score is associated with a less favorable outcome. In one embodiment, the gene expression data used in this method is obtained using a microarray.

In another embodiment, the present invention provides another method for predicting survival in a follicular lymphoma (FL) subject. In this method, a biopsy sample is obtained from the subject and gene expression data is obtained from the biopsy sample. The expression level of those genes belonging to a B cell differentiation, T-cell, or macrophage gene expression signature are averaged to generate gene expression signature values for each signature. A survival predictor score is then calculated using an equation: [2.053*(macrophage gene expression signature value)]−[2.344*(T-cell gene expression signature value)]−[0.729*(B-cell gene expression signature value)]. A higher survival predictor score is associated with a less favorable outcome. In one embodiment, the gene expression data used in this method is obtained using a microarray.

In another embodiment, the present invention provides yet another method for predicting survival in a follicular lymphoma (FL) subject. In this method, a biopsy sample is obtained from the subject and gene expression data is obtained from the biopsy sample. The expression level of those genes belonging to a macrophage, T-cell, or B-cell differentiation gene expression signature are averaged to generate gene expression signature values for each signature. A survival predictor score is then calculated using an equation: [1.51*(macrophage gene expression signature value)]−[2.11*(T-cell gene expression signature value)]−[0.505*(B-cell differentiation gene expression signature value)]. A higher survival predictor score is associated with a less favorable outcome. In one embodiment, the gene expression data used in this method is obtained using a microarray.

In another embodiment, the present invention provides a method for predicting survival in a diffuse large B cell lymphoma (DLBCL) subject. In this method, a biopsy sample is obtained from the subject and gene expression data is obtained from the biopsy sample. The expression level of those genes belonging to an ABC DLBCL high, lymph node, or MHC class II gene expression signature are averaged to generate gene expression signature values for each signature. A survival predictor score is then calculated using an equation: [0.586*(ABC DLBCL high gene expression signature value)]−[0.468*(lymph node gene expression signature value)]−[0.336*(MHC class II gene expression signature value)]. A higher survival predictor score is associated with a less favorable outcome. In one embodiment, the gene expression data used in this method is obtained using a microarray.

In another embodiment, the present invention provides another method for predicting survival in a diffuse large B cell lymphoma (DLBCL) subject. In this method, a biopsy sample is obtained from the subject and gene expression data is obtained from the biopsy sample. The expression level of those genes belonging to a lymph node, germinal B cell, proliferation, or MHC class II gene expression signature are averaged to generate gene expression signature values for each signature. A survival predictor score is then calculated using an equation: [−0.4337*(lymph node gene expression signature)]+[0.09*(proliferation gene expression signature)]−[0.4144*(germinal center B-cell gene expression signature)]−[0.2006*(MHC class II gene expression signature)]. A higher survival predictor score is associated with a less favorable outcome. In one embodiment, the gene expression data used in this method is obtained using a microarray.

In another embodiment, the present invention provides yet another method for predicting survival in a diffuse large B cell lymphoma (DLBCL) subject. In this method, a biopsy sample is obtained from the subject and gene expression data is obtained from the biopsy sample. The expression level of those genes belonging to a lymph node, germinal B cell, or MHC class II gene expression signature are averaged to generate gene expression signature values for each signature. A survival predictor score is then calculated using an equation: [−0.32*(lymph node gene expression signature)]−[0.176*(germinal B cell gene expression signature)]−[0.206*(MHC class II gene expression signature)]. A higher survival predictor score is associated with a less favorable outcome. In one embodiment, the gene expression data used in this method is obtained using a microarray. In another embodiment, the gene expression data is obtained using RT-PCR.

In another embodiment, the present invention provides a method for predicting survival in a mantle cell lymphoma (MCL) subject. In this method, a biopsy sample is obtained from the subject and gene expression data is obtained from the biopsy sample. The expression level of those genes belonging to a proliferation gene expression signature are averaged to generate a gene expression signature value. A survival predictor score is then calculated using an equation: [1.66*(proliferation gene expression signature value)]. A higher survival predictor score is associated with a less favorable outcome. In one embodiment, the gene expression data used in this method is obtained using a microarray.

In another embodiment, the present invention provides a method for determining the probability that a sample X belongs to a first lymphoma type or a second lymphoma type. In this method, a set of genes is identified that is differentially expressed between the two lymphoma types in question, and a set of scale factors representing the difference in expression between the lymphoma types for each of these genes are calculated. A series of linear predictor scores are generated for samples belonging to either of the two lymphoma types based on expression of these genes. Gene expression data is then obtained for sample X, and a linear predictor score is calculated for this sample. The probability that sample X belongs to the first lymphoma type is calculated using an equation that incorporates the linear predictor score of sample X and the mean and variance of the linear predictor scores for the known samples of either lymphoma type.

In another embodiment, the present invention provides a method for determining the lymphoma type of a sample X. In this method, a set of genes is identified that is differentially expressed between a first lymphoma type and a second lymphoma type, and a set of scale factors representing the difference in expression of each of these genes between the two lymphoma types are calculated. A series of linear predictor scores are generated for samples belonging to either of the two lymphoma types based on expression of these genes. Gene expression data is then obtained for sample X, and a linear predictor score is calculated for this sample. The probability that sample X belongs to the first lymphoma type is calculated using an equation that incorporates the linear predictor score of sample X and the mean and variance of the linear predictor scores for the known samples of either lymphoma type. This entire process is then repeated with various lymphoma types being substituted for the first lymphoma type, the second lymphoma type, or both.

In another embodiment, the present invention provides another method for determining the lymphoma type of a sample X. In this method, a series of lymphoma type pairs are created, with each pair consisting of a first lymphoma type and a second lymphoma type. For each type pair, gene expression data is obtained for a set of genes, and a series of scale factors representing the difference in expression of each of these genes between the two lymphoma types are calculated. A subset of z genes with the largest scale factors are identified, and a series of linear predictor scores are generated for samples belonging to either of the two lymphoma types. Linear predictor scores are calculated for anywhere from 1 to z of these genes. The number of genes from 1 to z that results in the largest difference in linear predictor scores between the two lymphoma types is selected, and gene expression data for these genes is obtained for sample X. A linear predictor score is generated for sample X, and the probability that the sample belongs to the first lymphoma type is calculated using an equation that incorporates the linear predictor score for sample X and the mean and variance of the linear predictor scores for the known samples of either lymphoma type.

In another embodiment, the present invention provides another method for determining the lymphoma type of a sample X. In this method, a series of lymphoma type pairs are created, with each pair consisting of a first lymphoma type and a second lymphoma type. For each type pair, gene expression data is obtained for a set of genes, and a series of scale factors representing the difference in expression of each of these genes between the two lymphoma types are calculated. The set of genes is divided into gene-list categories indicating correlation with a gene expression signature. Within each gene-list category, a subset of z genes with the largest scale factors are identified, and a series of linear predictor scores are generated for samples belonging to either of the two lymphoma types. Linear predictor scores are calculated for anywhere from 1 to z of these genes. The number of genes from 1 to z that results in the largest difference in linear predictor scores between the two lymphoma types is selected, and gene expression data for these genes is obtained for sample X. A linear predictor score is generated for sample X, and the probability q that the sample belongs to the first lymphoma type is calculated using an equation that incorporates the linear predictor score for sample X and the mean and variance of the linear predictor scores for the known samples of either lymphoma type. A high probability q indicates that sample X belongs to the first lymphoma type, a low probability q indicates that sample X belongs to the second lymphoma type, and a middle probability q indicates that sample X belongs to neither lymphoma type. The cut-off point between high, middle, and low probability values is determined by ranking samples of known lymphoma type according to their probability values, then analyzing every possible cut-off point between adjacent samples using the equation: 3.99*[(% of first lymphoma type misidentified as second lymphoma type)+(% of second lymphoma type misidentified as a first lymphoma type)]+[(% of first lymphoma type identified as belonging to neither lymphoma type)+(% of second lymphoma type identified as belonging to neither lymphoma type)]. The final cut-off points are those that minimize the value of this equation.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1: Method for identifying lymphoma type. Flow chart depicts a general method for identifying lymphoma type using gene expression data.

FIG. 2: Survival signature analysis. Flow chart depicts method for developing a lymphoma survival predictor based on gene expression patterns.

FIG. 3: FL survival data. Survival data for 191 subjects diagnosed with FL. Median age at diagnosis was 51 years (ranging from 23 to 81 years), and the subjects had a median follow-up of 6.6 years (8.1 years for survivors, with a range of <1 to 28.2 years).

FIG. 4: Hierarchical clustering of survival associated genes in FL samples. Each column represents a single FL sample, while each row represents a single gene. Relative gene expression is depicted according to the scale at the bottom of the figure. The dendrogram to the left indicates the degree to which the expression pattern of each gene is correlated with that of the other genes. The bars indicate sets of coordinately regulated genes defined as gene expression signatures. Genes comprising the immune response-1 and immune response-2 gene expression signature are listed on the right.

FIG. 5: Kaplan-Meier plot of survival in FL samples based on survival predictor scores. 191 FL samples were divided into quartiles based on their survival predictor scores. The survival predictor scores were calculated using the equation: [(2.71*immune response-2 gene expression signature value)]−[(2.36×immune response-1 gene expression signature value)].

FIG. 6: Kaplan-Meier plot of survival in FL samples based on IPI score. 96 FL samples were divided into three groups based on their IPI scores.

FIG. 7: Kaplan-Meier plot of survival in FL samples with low or high risk IPI scores based on survival predictor scores. 96 FL samples with low risk (left panel) or intermediate risk (right panel) IPI scores were divided into quartiles based on their survival predictor scores. The survival predictor scores were calculated using the equation: [(2.71*immune response-2 gene expression signature value)]−[(2.36×immune response-1 gene expression signature value)].

FIG. 8: Kaplan-Meier plot of survival in FL samples based on survival predictor scores. 191 FL samples were divided into quartiles based on their survival predictor scores. The survival predictor scores were calculated using the equation: [2.053*(macrophage gene expression signature value)]−[2.344*(T-cell gene expression signature value)]−[0.729*(B-cell differentiation gene expression signature value)].

FIG. 9: Kaplan-Meier plot of survival in FL samples based on survival predictor scores. 191 FL samples were divided into quartiles based on their survival predictor scores. The survival predictor scores were calculated using the equation: [1.51*(macrophage gene expression signature value)]−[2.11*(T-cell gene expression signature value)]−[0.505*(B-cell differentiation gene expression signature value)].

FIG. 10: Kaplan-Meier plot of survival in DLBCL samples based on survival predictor scores. 231 DLBCL samples were divided into quartiles based on their survival predictor scores. The survival predictor scores were calculated using the equation: [0.586*(ABC DLBCL high gene expression signature value)]−[0.468*(lymph node gene expression signature value)]−[(0.336*MHC Class II gene expression signature value)].

FIG. 11: Kaplan-Meier plot of survival in DLBCL samples based on survival predictor scores. 200 DLBCL samples were divided into quartiles based on their survival predictor scores. The survival predictor scores were calculated using the equation: [−0.4337*(lymph node gene expression signature value)]+[0.09*(proliferation gene expression signature value)]−[0.4144*(germinal center B-cell gene expression signature value)]−[0.2006*(MHC class II gene expression signature value)].

FIG. 12: Kaplan-Meier plot of survival in DLBCL samples based on survival predictor scores. 200 DLBCL samples were divided into quartiles based on their survival predictor scores. The survival predictor scores were calculated using the equation: [−0.32*(lymph node gene expression signature value)]−[0.176*(germinal center B-cell gene expression signature value)]−[0.206*(MHC class II gene expression signature value)].

FIG. 13: Kaplan-Meier plot of survival in MCL samples based on survival predictor scores. 21 MCL samples were divided into two equivalent groups based on their survival predictor scores. The survival predictor scores were calculated using the equation: 1.66*(proliferation gene expression signature value).

FIG. 14: Kaplan-Meier plot of survival in MCL samples based on survival predictor scores. 21 MCL samples were divided into two equivalent groups based on their survival predictor scores. The survival predictor scores were calculated using the equation: 1.66*(proliferation gene expression signature value).

FIG. 15: Predicting lymphoma type using Bayesian analysis. Bayes' rule can be used to determine the probability that an unknown sample belongs to a first lymphoma type rather than a second lymphoma type. A linear predictor score is generated for the sample, and the probability that the sample belongs to the first lymphoma type is determined based on the distribution of linear predictor scores within the first and second lymphoma type.

FIG. 16: Performance of MCL predictor model. Results of the gene-expression based predictor model for MCL are shown for three models (MCL vs. ABC, MCL vs. GCB, MCL vs. SLL). Performance is shown for both the training set and the validation set.

FIG. 17: Gene expression-based identification of DLBCL. Expression levels for 27 genes in a subgroup predictor are shown for 274 DLBCL samples. Expression levels are depicted according to the scale shown at the left. The 14 genes used to predict the DLBCL subgroups in the Affymetrix data set are indicated with asterisks. The probabilities that the DLBCL samples belong to the ABC or GCB subtypes are graphed at the top, and the DLBCL cases are arranged accordingly. Cases belonging to either ABC or GCB with 90% or greater probability are indicated.

FIG. 18: Performance of DLBCL subtype predictor model. Assignments of DLBCL samples to the ABC or GCB subtypes based on hierarchical clustering vs. the predictor model disclosed herein are compared within the training, validation, and total set of samples.

FIG. 19: Relationship of gene expression in normal B cell subpopulations to DLBCL subtypes. Relative gene expression in the indicated purified B cell populations is depicted according to the scale in FIG. 17. The P value of the difference in expression of these genes between the GCB and ABC DLBCL subtypes is shown, and the subtype with the higher expression is shown is indicated (blue, ABC; orange, GCB). A. DLBCL subtype distinction genes that are more highly expressed in germinal center B cells than at other B cell differentiation stages. B. DLBCL subtype distinction genes that are more highly expressed in plasma cells than at other B cell differentiation stages.

FIG. 20: Identification of a PMBL gene expression signature. A. Hierarchical clustering identified a set of 23 PMBL signature genes that were more highly expressed in most lymphomas with a clinical diagnosis of PMBL than in lymphomas assigned to the GCB or ABC subtypes. Each row presents gene expression measurements from a single Lymphochip microarray feature representing the genes indicated. Each column represents a single lymphoma biopsy sample. Relative gene expression is depicted according to the scale shown. B. Hierarchical clustering of the lymphoma biopsy samples based on expression of the PMBL signature genes identified in (A). A “core” cluster of lymphoma cases was identified that highly expressed the PMBL signature genes.

FIG. 21: Development of a gene expression-based molecular diagnosis of PMBL. A. A PMBL predictor was created based on expression of the 46 genes shown. Relative gene expression for each lymphoma biopsy sample is presented according to the scale shown in FIG. 20. The probability that each sample is PMBL or DLBCL based on gene expression is shown at the top. B. The PMBL predictor was used to classify 274 lymphoma samples as PMBL or DLBCL. Prediction results are summarized on the right, and the relative gene expression for each case that was classified by the predictor as PMBL is shown on the left. Average expression of each gene in samples classified as DLBCL is also shown. The 20 genes listed are those represented on the Lymphochip that were more highly expressed in PMBL than in DLBCL. Not shown are eight genes from the PMBL predictor that were more highly expressed in DLBCL than in PMBL.

FIG. 22: Clinical characteristics of PMBL patients. Kaplan-Meier plot of overall survival in PMBL, GCB, and ABC patients after chemotherapy.

FIG. 23: Optimization of gene number in lymphoma predictor. The optimal number of genes for inclusion in the lymphoma type predictor model is that number which generates a maximum t-statistic when comparing the LPS of two samples from different lymphoma types.

FIG. 24: LPS distribution among FL and DLBCL/BL samples. Standard and proliferation LPSs for FL (×) and DLBCL/BL (+) samples. Dotted lines indicate standard deviations from the fitted multivariate normal distributions.

FIG. 25: Determination of cut-off points for lymphoma classification. The cut-off points between samples classified as DLBCL/BL, FL, or unclassified were optimized to minimize the number of samples classified as the wrong lymphoma type. The optimal lower cut-off point was at q=0.49, while the optimal upper cut-off point was at q=0.84.

FIG. 26: Division of LPSs among FL and DLBCL/FL samples. Illustration of how the cut-off points described in FIG. 25 divided the space between the LPSs of FL (×) and DLBCL/BL (+) samples.

FIG. 27: Lymphoma classification results. Results of lymphoma classification based on gene expression. 100% of SLL, MCL, and FH samples were classified correctly, and only 3% of DLBCL/BL and FL samples were classified incorrectly.

FIG. 28: DLBCL classification results. Results of DLBCL subtype classification based on gene expression. None of the ABC samples were classified as the wrong subtype, while only one of the BL samples was classified incorrectly. Of the GCB and PMBL samples, only 5% and 6%, respectively, were classified incorrectly.

DETAILED DESCRIPTION

The following description of the invention is merely intended to illustrate various embodiments of the invention. As such, the specific modifications discussed are not to be construed as limitations on the scope of the invention. It will be apparent to one skilled in the art that various equivalents, changes, and modifications may be made without departing from the scope of the invention, and it us understood that such equivalent embodiments are to be included herein.

Gene expression profiling of a cancer cell or biopsy reflects the molecular phenotype of a cancer at the time of diagnosis. As a consequence, the detailed picture provided by the genomic expression pattern provides the basis for a new systematic classification of cancers and more accurate predictors of survival and response to treatment. The present invention discloses methods for identifying, diagnosing, and/or classifying a lymphoma, lymphoid malignancy, or lymphoproliferative disorder based on its gene expression patterns. The present invention also discloses methods for predicting survival in a subject diagnosed with a particular lymphoma type or subtype using gene expression data. The information obtained using these methods will be useful in evaluating the optimal therapeutic approach to be employed with regards to a particular subject.

The term “lymphoproliferative disorder” as used herein refers to any tumor of lymphocytes, and may refer to both malignant and benign tumors. The terms “lymphoma” and “lymphoid malignancy” as used herein refer specifically to malignant tumors derived from lymphocytes and lymphoblasts. Examples of lymphomas include, but are not limited to, follicular lymphoma (FL), Burkitt lymphoma (BL), mantle cell lymphoma (MCL), follicular hyperplasia (FH), small cell lymphocytic lymphoma (SLL), mucosa-associated lymphoid tissue lymphoma (MALT), splenic lymphoma, multiple myeloma, lymphoplasmacytic lymphoma, post-transplant lymphoproliferative disorder (PTLD), lymphoblastic lymphoma, nodal marginal zone lymphoma (NMZ), germinal center B cell-like diffuse large B cell lymphoma (GCB), activated B cell-like diffuse large B cell lymphoma (ABC) and primary mediastinal B cell lymphoma (PMBL).

The phrase “lymphoma type” (or simply “type”) as used herein refers to a diagnostic classification of a lymphoma. The phrase may refer to a broad lymphoma class (e.g., DLBCL, FL, MCL, etc.) or to a subtype or subgroup falling within a broad lymphoma class (e.g., GCB DLBCL, ABC DLBCL).

The phrase “gene expression data” as used herein refers to information regarding the relative or absolute level of expression of a gene or set of genes in a cell or group of cells. The level of expression of a gene may be determined based on the level of RNA, such as mRNA, encoded by the gene. Alternatively, the level of expression may be determined based on the level of a polypeptide or fragment thereof encoded by the gene. “Gene expression data” may be acquired for an individual cell, or for a group of cells such as a tumor or biopsy sample.

The term “microarray,” “array,” or “chip” refers to a plurality of nucleic acid probes coupled to the surface of a substrate in different known locations. The substrate is preferably solid. Microarrays have been generally described in the art in, for example, U.S. Pat. No. 5,143,854 (Pirrung), U.S. Pat. No. 5,424,186 (Fodor), U.S. Pat. No. 5,445,934 (Fodor), U.S. Pat. No. 5,677,195 (Winkler), U.S. Pat. No. 5,744,305 (Fodor), U.S. Pat. No. 5,800,992 (Fodor), U.S. Pat. No. 6,040,193 (Winkler), and Fodor et al. 1991. Light-directed, spatially addressable parallel chemical synthesis. Science, 251:767-777. Each of these references is incorporated by reference herein in their entirety.

The term “gene expression signature” or “signature” as used herein refers to a group of coordinately expressed genes. The genes making up this signature may be expressed in a specific cell lineage, stage of differentiation, or during a particular biological response. The genes can reflect biological aspects of the tumors in which they are expressed, such as the cell of origin of the cancer, the nature of the non-malignant cells in the biopsy, and the oncogenic mechanisms responsible for the cancer (Shaffer 2001). Examples of gene expression signatures include lymph node (Shaffer 2001), proliferation (Rosenwald 2002), MHC class II, ABC DLBCL high, B-cell differentiation, T-cell, macrophage, immune response-1, immune response-2, and germinal center B cell.

The phrase “survival predictor score” as used herein refers to a score generated by a multivariate model used to predict survival based on gene expression. A subject with a higher survival predictor score is predicted to have poorer survival than a subject with a lower survival predictor score.

The term “survival” as used herein may refer to the probability or likelihood of a subject surviving for a particular period of time. Alternatively, it may refer to the likely term of survival for a subject, such as expected mean or median survival time for a subject with a particular gene expression pattern.

The phrase “linear predictor score” or “LPS” as used herein refers to a score that denotes the probability that a sample belongs to a particular lymphoma type. An LPS may be calculated using an equation such as:

${{{LPS}(S)} = {\sum\limits_{j \in G}{t_{j}S_{j}}}},$ where S_(j) is the expression of gene j from gene set G in a sample S, and t_(j) is a scale factor representing the difference in expression of gene j between a first lymphoma type and a second lymphoma type. Alternatively, a linear predictor score may be generated by other methods including but not limited to linear discriminant analysis (Dudoit 2002), support vector machines (Furey 2000), or shrunken centroids (Tibshirani 2002)

The phrase “scale factor” as used herein refers to a factor that defines the relative difference in expression of a particular gene between two samples. An example of a scale factor is a t-score generated by a Student's t-test.

The phrase “lymphoma subject,” wherein “lymphoma” is a specific lymphoma type (e.g., “follicular lymphoma subject”), may refer to a subject that has been diagnosed with a particular lymphoma by any method known in the art or discussed herein. This phrase may also refer to a subject with a known or suspected predisposition or risk of developing a particular lymphoma type.

The pattern of expression of a particular gene is closely connected to the biological role and effect of its gene product. For this reason, the systematic study of variations in gene expression provides an alternative approach for linking specific genes with specific diseases and for recognizing heritable gene variations that are important for immune function. For example, allelic differences in the regulatory region of a gene may influence the expression levels of that gene. An appreciation for such quantitative traits in the immune system may help elucidate the genetics of autoimmune diseases and lymphoproliferative disorders.

Genes that encode components of the same multi-subunit protein complex are often coordinately regulated. Coordinate regulation is also observed among genes whose products function in a common differentiation program or in the same physiological response pathway. Recent application of gene expression profiling to the immune system has shown that lymphocyte differentiation and activation are accompanied by parallel changes in expression among hundreds of genes. Gene expression databases may be used to interpret the pathological changes in gene expression that accompany autoimmunity, immune deficiencies, cancers of immune cells and of normal immune responses.

Scanning and interpreting large bodies of relative gene expression data is a formidable task. This task is greatly facilitated by algorithms designed to organize the data in a way that highlights systematic features, and by visualization tools that represent the differential expression of each gene as varying intensities and hues of color (Eisen 1998). The development of microarrays, which are capable of generating massive amounts of expression data in a single experiment, has greatly increased the need for faster and more efficient methods of analyzing large-scale expression data sets. In order to effectively utilize microarray gene expression data for the identification and diagnosis of lymphoma and for the prediction of survival in lymphoma patients, new algorithms must be developed to identify important information and convert it to a more manageable format. In addition, the microarrays used to generate this data should be streamlined to incorporate probe sets that are useful for diagnosis and survival prediction. Embodiments of the present invention disclose methods and compositions that address both of these considerations.

The mathematical analysis of gene expression data is a rapidly evolving science based on a rich mathematics of pattern recognition developed in other contexts (Kohonen 1997). Mathematical analysis of gene expression generally has three goals. First, it may be used to identify groups of genes that are coordinately regulated within a biological system. Second, it may be used to recognize and interpret similarities between biological samples on the basis of similarities in gene expression patterns. Third, it may be used to recognize and identify those features of a gene expression pattern that are related to distinct biological processes or phenotypes.

Mathematical analysis of gene expression data often begins by establishing the expression pattern for each gene on an array across n experimental samples. The expression pattern of each gene can be represented by a point in n-dimensional space, with each coordinate specified by an expression measurement in one of the n samples (Eisen 1998). A clustering algorithm that uses distance metrics can then be applied to locate clusters of genes in this n-dimensional space. These clusters indicate genes with similar patterns of variation in expression over a series of experiments. Clustering methods that have been applied to microarray data in the past include hierarchical clustering (Eisen 1998), self-organizing maps (SOMs) (Tamayo 1999), k-means (Tavazoie 1999), and deterministic annealing (Alon 1999). A variety of different algorithms, each emphasizing distinct orderly features of the data, may be required to glean the maximal biological insight from a set of samples (Alizadeh 1998). One such algorithm, hierarchical clustering, begins by determining the gene expression correlation coefficients for each pair of the n genes studied. Genes with similar gene expression correlation coefficients are grouped next to one another in a hierarchical fashion. Generally, genes with similar expression patterns under a particular set of conditions encode protein products that play related roles in the physiological adaptation to those conditions. Novel genes of unknown function that are clustered with a large group of functionally related genes are likely to participate in the same biological process. Likewise, the other clustering methods mentioned herein may also group genes together that encode proteins with related biological function.

Gene expression maps may be constructed by organizing the gene expression data from multiple samples using any of the various clustering algorithms outlined herein. The ordered tables of data may then be displayed graphically in a way that allows researchers and clinicians to assimilate both the choreography of gene expression on a broad scale and the fine distinctions in expression of individual genes.

In such a gene expression map, genes that are clustered together reflect a particular biological function, and are termed gene expression signatures (Shaffer 2001). One general type of gene expression signature includes genes that are characteristically expressed in a particular cell type or at a particular stage of cellular differentiation or activation. Another general type of gene expression signature includes genes that are regulated in their expression by a particular biological process such as proliferation, or by the activity of a particular transcription factor or signaling pathway.

The pattern of gene expression in a biological sample provides a distinctive and accessible molecular picture of its functional state and identity (DeRisi 1997; Cho 1998; Chu 1998; Holstege 1998; Spellman 1998). Each cell transduces variation in its environment, internal state, and developmental state into readily measured and recognizable variation in gene expression patterns. Two different samples that have related gene expression patterns are therefore likely to be biologically and functionally similar to one another. Some biological processes are reflected by the expression of genes in a gene expression signature, as described above. The expression of gene expression signatures in a particular sample can provide important biological insights regarding its cellular composition and the function of various intracellular pathways within the cells.

The present invention discloses a variety of gene expression signatures related to the clinical outcome of lymphoma patients. While several of these signatures share a name with a previously disclosed signature, each of the gene expression signatures disclosed herein comprises a novel combination of genes. For example, the lymph node signature disclosed herein includes genes encoding extracellular matrix components and genes that are characteristically expressed in macrophage, NK, and T cells (e.g., α-Actinin, collagen type III α 1, connective tissue growth factor, fibronectin, KIAA0233, urokinase plasminogen activator). The proliferation signature includes genes that are characteristically expressed by cells that are rapidly multiplying or proliferating (e.g., c-myc, E21G3, NPM3, BMP6). The MHC class II signature includes genes that interact with lymphocytes in order to allow the recognition of foreign antigens (e.g., HLA-DPα, HLA-DQα, HLA-DRα, HLA-DRβ). The immune response-1 signature includes genes encoding T cell markers (e.g., CD7, CD8B1, ITK, LEF1, STAT4), as well as genes that are highly expressed in macrophages (e.g., ACTN1, TNFSF13B). The immune response-2 signature includes genes known to be preferentially expressed in macrophages and/or dendritic cells (e.g., TLR5, FCGR1A, SEPT10, LGMN, C3AR1). The germinal center B cell signature includes genes known to be overexpressed at this stage of B cell differentiation (e.g., MME, MEF2C, BCL6, LMO2, PRSPAP2, MBD4, EBF, MYBL1.

Databases of gene expression signatures have proven quite useful in elucidating the complex gene expression patterns of various cancers. For example, expression of genes from the germinal center B-cell signature in a lymphoma biopsy suggests that the lymphoma is derived from this stage of B cell differentiation. In the same lymphoma-biopsy, the expression of genes from the T cell signature can be used to estimate the degree of infiltration of the tumor by host T cells, while the expression of genes from the proliferation signature can be used to quantitate the tumor cell proliferation rate. In this manner, gene expression signatures provide an “executive summary” of the biological properties of a tumor specimen. Gene expression signatures can also be helpful in interpreting the results of a supervised analysis of gene expression data. Supervised analysis generates a long list of genes with expression patterns that are correlated with survival. Gene expression signatures can be useful in assigning these “predictive” genes to functional categories. In building a multivariate model of survival based on gene expression data, this functional categorization helps to limit the inclusion of multiple genes in the model that measure the same aspect of tumor biology.

Gene expression profiles can be used to create multivariate models for predicting survival. The methods for creating these models are called “supervised” because they use clinical data to guide the selection of genes to be used in the prognostic classification. For example, a supervised method might identify genes with expression patterns that correlate with the length of overall survival following chemotherapy. The general method used to create a multivariate model for predicting survival may utilize the following steps:

-   -   1. Identify genes with expression patterns that are univariately         associated with a particular clinical outcome using a Cox         proportional hazards model. Generally, a univariate p-value of         <0.01 is considered the cut-off for significance. These genes         are termed “predictor” genes.     -   2. Within a set of predictor genes, identify gene expression         signatures.     -   3. For each gene expression signature that is significantly         associated with survival, average the expression of the         component genes within this signature to generate a gene         expression signature value.     -   4. Build a multivariate Cox model of clinical outcome using the         gene expression signature values.     -   5. If possible, include additional genes in the model that do         not belong to a gene expression signature but which add to the         statistical power of the model.         This approach has been utilized in the present invention to         create novel survival prediction models for FL, DLBCL, and MCL.         Each of these models generates a survival predictor score, with         a higher score being associated with worse clinical outcome.         Each of these models may be used separately to predict survival.         Alternatively, these models may be used in conjunction with one         or more other models, disclosed herein or in other references,         to predict survival.

A first FL survival predictor was generated using gene expression data obtained using Affymetrix U133A and U133B microarrays. This predictor incorporated immune response-1 and immune response-2 gene expression signatures. Fitting the Cox proportional hazards model to the gene expression signature values obtained from these signatures resulted in the following model: Survival predictor score=[(2.71*immune response-2 gene expression signature value)]−[(2.36×immune response-1 gene expression signature value)].

A second FL survival predictor was generated using gene expression data obtained using Affymetrix U133A and U133B microarrays. This predictor incorporated macrophage, T-cell, and B-cell differentiation gene expression signatures. Fitting the Cox proportional hazards model to the gene expression signature values obtained from these signatures resulted in the following model: Survival predictor score=[2.053*(macrophage gene expression signature value)]−[2.344*(T-cell gene expression signature value)]−[0.729*(B-cell differentiation gene expression signature value)].

A third FL survival predictor was generated using gene expression data obtained using the Lymph Dx microarray. This predictor incorporated macrophage, T-cell, and B-cell differentiation gene expression signatures. Fitting the Cox proportional hazards model to the gene expression signature values obtained from these signatures resulted in the following model: Survival predictor score=[1.51*(macrophage gene expression signature value)]−[2.11*(T-cell gene expression signature value)]−[0.505*(B-cell differentiation gene expression signature value)].

A first DLBCL survival predictor was generated using gene expression data obtained using Affymetrix U133A and U133B microarrays. This predictor incorporated ABC DLBCL high, lymph node, and MHC class II gene expression signatures. Fitting the Cox proportional hazards model to the gene expression signature values obtained from these signatures resulted in the following model: Survival predictor score=[0.586*(ABC DLBCL high gene expression signature value)]−[0.468*(lymph node gene expression signature value)]−[0.336*(MHC class II gene expression signature value)].

A second DLBCL survival predictor was generated using gene expression data obtained using the Lymph Dx microarray. This predictor incorporated lymph node, proliferation, germinal center B-cell, and MHC class II gene expression signatures. Fitting the Cox proportional hazards model to the gene expression signature values obtained from these signatures resulted in the following model: Survival predictor score=[−0.4337*(lymph node gene expression signature value)]+[0.09*(proliferation gene expression signature value)]−[0.4144*(germinal center B-cell gene expression signature value)]−[0.2006*(MHC class II gene expression signature value)].

A third DLBCL survival predictor was generated using gene expression data obtained using the Lymph Dx microarray. This predictor incorporated lymph node, germinal center B cell, and MHC class II gene expression signatures. Fitting the Cox proportional hazards model to the gene expression signature values obtained from these signatures resulted in the following model: Survival predictor score=[−0.32*(lymph node gene expression signature value)]−[0.176*(germinal center B-cell gene expression signature value)]−[0.206*(MHC class II gene expression signature value)].

An MCL survival predictor was generated using gene expression data obtained using Affymetrix U133A, Affymetrix U133B, and Lymph Dx microarrays. This predictor incorporated a proliferation gene expression signature. Fitting the Cox proportional hazards model to the gene expression signature values obtained from these signatures resulted in the following model: Survival predictor score=[1.66*(proliferation gene expression signature value)].

Gene expression data can also be used to diagnose and identify lymphoma types. In an embodiment of the present invention, a statistical method based on Bayesian analysis was developed to classify lymphoma specimens according to their gene expression profiles. This method does not merely assign a tumor to a particular lymphoma type, but also determines the probability that the tumor belongs to that lymphoma type. Many different methods have been formulated to predict cancer subgroups (Golub 1999; Ramaswamy 2001; Dudoit 2002; Radmacher 2002). These methods assign tumors to one of two subgroups based on expression of a set of differentially expressed genes. However, they do not provide a probability of membership in a subgroup. By contrast, the method disclosed herein used Bayes' rule to estimate this probability, thus allowing one to vary the probability cut-off for assignment of a tumor to a particular subgroup. In tumor types in which unknown additional subgroups may exist, the present method allows samples that do not meet the gene expression criteria of known subgroups to fall into an unclassified group with intermediate probability. A cancer subgroup predictor of the type described herein may be used clinically to provide quantitative diagnostic information for an individual cancer patient. This information can in turn be used to provide a predictor of treatment outcome for a particular cancer patient.

For any two lymphoma types A and B, there is a set of genes with significantly higher expression in type A than type B, and a set of genes with significantly lower expression in type A than in type B. By observing the expression of these genes in an unknown sample, it is possible to determine to which of the two types the sample belongs. Evaluating the likelihood that a particular sample belongs to one or the other lymphoma type by Bayesian analysis may be done using the following steps:

-   -   1) Identify those genes that are most differentially expressed         between the two lymphoma types. This can be done by selecting         those genes with the largest t-statistic between the two         lymphoma types. The genes in this step may be subdivided into         gene expression signatures in certain cases, with genes from         each signature analyzed separately.     -   2) Create a series of linear predictor score (LPS) for samples         belonging to either lymphoma type.     -   3) Evaluate the LPS for each sample in a training set, and         estimate the distribution of these scores within each lymphoma         type according to a normal distribution.     -   4) Use Bayes' rule to evaluate the probability that each         subsequent sample belongs to one or the other lymphoma type.         If only two types of lymphoma are being distinguished, then a         single probability score is sufficient to discriminate between         the two types. However, if more than two lymphoma types are         being distinguished, multiple scores will be needed to highlight         specific differences between the types.

In an embodiment of the present invention, a novel microarray entitled the Lymph Dx microarray was developed for the identification and diagnosis of lymphoma types. The Lymph Dx microarray contains cDNA probes corresponding to approximately 2,653 genes, fewer than the number seen on microarrays that have been used previously for lymphoma diagnosis. The reduced number of probes on the Lymph Dx microarray is the result of eliminating genes that are less useful for the identification of lymphoma types and predicting clinical outcome. This reduction allows for simplified analysis of gene expression data. The genes represented on the Lymph Dx microarray can be divided into four broad categories: 1,101 lymphoma predictor genes identified previously using the Affymetrix U133 microarray, 171 outcome predictor genes, 167 new genes not found on the Affymetrix U133 microarray, and 1,121 named genes. A list of the probe sets on the Lymph Dx microarray is presented in Table 2, contained in the file “Table_(—)0002_LymphDx_Probe_List.txt.”

In an embodiment of the present invention, gene expression data obtained using the Lymph Dx microarray was used to identify and classify lymphomas using Bayesian analysis. This method was similar to that outlined above, but included additional steps designed to optimize the number of genes used and the cut-off points between lymphoma types. A general overview of this method is presented in FIG. 1. Each gene represented on the Lymph Dx microarray was placed into one of three gene-list categories based on its correlation with the lymph node or proliferation gene expression signatures: lymph node, proliferation, or standard. These signatures were identified by clustering of the DLBCL cases using hierarchical clustering and centroid-correlation of 0.35. Standard genes were those with expression patterns that did not correlate highly with expression of the lymph node or proliferation signatures. Lymph Dx gene expression data was first used to identify samples as FL, MCL, SLL, FH, or DLBCL/BL, then to identify DLBCL/BL samples as ABC, GCB, PMBL, or BL. For each stage, a series of pair-wise models was created, with each model containing a different pair of lymphoma types (e.g., FL vs. MCL, SLL vs. FH, etc.). For each pair, the difference in expression of each gene on the microarray was measured, and a t-statistic was generated representing this difference. Genes from each gene-list category were ordered based on their t-statistic, and those with the largest t-statistics were used to generate a series of LPSs for samples belonging to either lymphoma type. The number of genes used to generate the LPSs was optimized by repeating the calculation using between five and 100 genes from each gene-list category. The number of genes from each category used in the final LPS calculation was that which gave rise to the largest difference in LPS between the two lymphoma types. Once the number of genes in each gene-list category was optimized, four different LPSs were calculated for each sample. The first included genes from the standard gene-list category only, the second included genes from the proliferation and standard gene-list categories, the third included genes from the lymph node and standard gene-list categories, and the fourth included genes from all three categories. The probability q that a sample X belongs to the first lymphoma type of a pair-wise model can then be calculated using an equation:

$q = \frac{\phi\left( {{{{LPS}(X)};{\hat{\mu}}_{1}},{\hat{\sigma}}_{1}} \right)}{{\phi\left( {{{{LPS}(X)};{\hat{\mu}}_{1}},{\hat{\sigma}}_{1}} \right)} + {\phi\left( {{{{LPS}(X)};{\hat{\mu}}_{2}},{\hat{\sigma}}_{2}} \right)}}$ LPS(X) is the LPS for sample X, φ(x; μ, σ) is the normal density function with mean μ and standard deviation σ, {circumflex over (μ)}₁ and {circumflex over (σ)}₁ are the mean and variance of the LPSs for samples belonging to the first lymphoma type, and {circumflex over (μ)}₂ and {circumflex over (σ)}₂ are the mean and variance of the LPSs for samples belonging to the second lymphoma type. Samples with high q values were classified as the first lymphoma type, samples with low q values were classified as the second lymphoma type, and samples with middle range q values were deemed unclassified. To determine the proper cut-off point between high, low, and middle q values, every possible cut-off point between adjacent samples was analyzed by an equation: 3.99*[(% of type 1 misidentified as type 2)+(% of type 2 misidentified as type 1)]+[(% of type 1 unclassified)+(% of type 2 misidentified)]. This equation was used to favor the assignment of a sample to an “unclassified” category rather than to an incorrect lymphoma type. The final cut-off points were those which minimized this equation. The coefficient of 3.99 was chosen arbitrarily to allow an additional classification error only if the adjustment resulted in four or more unclassified samples becoming correctly classified. The coefficient can be varied to achieve a different set of trade-offs between the number of unclassified and misidentified samples.

To ensure that the accuracy of the model was not a result of overfitting, each model was validated by leave-one-out cross-validation. This entailed removing each sample of known lymphoma type from the data one at a time, and then determining whether the model could predict the missing sample. This process confirmed the accuracy of the prediction method.

The classification of a lymphoproliferative disorder in accordance with embodiments of the present invention may be used in combination with any other effective classification feature or set of features. For example, a disorder may be classified by a method of the present invention in conjunction with WHO suggested guidelines, morphological properties, histochemical properties, chromosomal structure, genetic mutation, cellular proliferation rates, immunoreactivity, clinical presentation, and/or response to chemical, biological, or other agents. Embodiments of the present invention may be used in lieu of or in conjunction with other methods for lymphoma diagnosis, such as immunohistochemistry, flow cytometry, FISH for translocations, or viral diagnostics.

Accurate determination of lymphoma type in a subject allows for better selection and application of therapeutic methods. Knowledge about the exact lymphoma affecting a subject allows a clinician to select therapies or treatments that are most appropriate and useful for that subject, while avoiding therapies that are nonproductive or even counterproductive. For example, CNS prophylaxis may be useful for treating BL but not DLBCL, CHOP treatment may be useful for treating DLBCL but not blastic MCL (Fisher 1993; Khouri 1998), and subjects with follicular lymphoma frequently receive treatment while subjects with follicular hyperplasia do not. In each of these situations, the lymphoma types or subtypes in question can be difficult to distinguish using prior art diagnostic methods. The diagnostic and identification methods of the present invention allow for more precise delineation between these lymphomas, which simplifies the decision of whether to pursue a particular therapeutic option. Likewise, the survival prediction methods disclosed in the present invention also allow for better selection of therapeutic options. A subject with a very low survival predictor score (i.e., very good prognosis) may not receive treatment, but may instead be subjected to periodic check-ups and diligent observation. As survival predictor scores increase (i.e., prognosis gets worse), subjects may receive more intensive treatments. Those subjects with the highest survival predictor scores (i.e., very poor prognosis) may receive experimental treatments or treatments with novel agents. Accurate survival prediction using the methods disclosed herein provides an improved tool for selecting treatment options and for predicting the likely clinical outcome of those options.

Any effective method of quantifying the expression of at least one gene, gene set, or group of gene sets may be used to acquire gene expression data for use in embodiments of the present invention. For example, gene expression data may be measured or estimated using one or more microarrays. The microarrays may be of any effective type, including but not limited to nucleic acid based or antibody based. Gene expression may also be measured by a variety of other techniques, including but not limited to PCR, quantitative RT-PCR, real-time PCR, RNA amplification, in situ hybridization, immunohistochemistry, immunocytochemistry, FACS, serial analysis of gene expression (SAGE) (Velculescu 1995), Northern blot hybridization, or western blot hybridization.

Nucleic acid microarrays generally comprise nucleic acid probes derived from individual genes and placed in an ordered array on a support. This support may be, for example, a glass slide, a nylon membrane, or a silicon wafer. Gene expression patterns in a sample are obtained by hybridizing the microarray with the gene expression product from the sample. This gene expression product may be, for example, total cellular mRNA, rRNA, or cDNA obtained by reverse transcription of total cellular mRNA. The gene expression product from a sample is labeled with a radioactive, fluorescent, or other label to allow for detection. Following hybridization, the microarray is washed, and hybridization of gene expression product to each nucleic acid probe on the microarray is detected and quantified using a detection device such as a phosphorimager or scanning confocal microscope.

There are two broad classes of microarrays: cDNA and oligonucleotide arrays. cDNA arrays consist of hundreds or thousands of cDNA probes immobilized on a solid support. These cDNA probes are usually 100 nucleotides or greater in size. There are two commonly used designs for cDNA arrays. The first is the nitrocellulose filter array, which is generally prepared by robotic spotting of purified DNA fragments or lysates of bacteria containing cDNA clones onto a nitrocellulose filter (Southern 1992; Southern 1994; Gress 1996; Pietu 1996). The other commonly used cDNA arrays is fabricated by robotic spotting of PCR fragments from cDNA clones onto glass microscope slides (Schena 1995; DeRisi 1996; Schena 1996; Shalon 1996; DeRisi 1997; Heller 1997; Lashkari 1997). These cDNA microarrays are simultaneously hybridized with two fluorescent cDNA probes, each labeled with a different fluorescent dye (typically Cy3 or Cy5). In this format, the relative mRNA expression in two samples is directly compared for each gene on the microarray. Oligonucleotide arrays differ from cDNA arrays in that the probes are 20- to 25-mer oligonucleotides. Oligonucleotide arrays are generally produced by in situ oligonucleotide synthesis in conjunction with photolithographic masking techniques (Pease 1994; Lipshutz 1995; Chee 1996; Lockhart 1996; Wodicka 1997). The solid support for oligonucleotide arrays is typically a glass or silicon surface.

Methods and techniques applicable to array synthesis and use have been described in, for example, U.S. Pat. No. 5,143,854 (Pirrung), U.S. Pat. No. 5,242,974 (Holmes), U.S. Pat. No. 5,252,743 (Barrett), U.S. Pat. No. 5,324,633 (Fodor), U.S. Pat. No. 5,384,261 (Winkler), U.S. Pat. No. 5,424,186 (Fodor), U.S. Pat. No. 5,445,934 (Fodor), U.S. Pat. No. 5,451,683 (Barrett), U.S. Pat. No. 5,482,867 (Barrett), U.S. Pat. No. 5,491,074 (Aldwin), U.S. Pat. No. 5,527,681 (Holmes), U.S. Pat. No. 5,550,215 (Holmes), U.S. Pat. No. 5,571,639 (Hubbell), U.S. Pat. No. 5,578,832 (Trulson), U.S. Pat. No. 5,593,839 (Hubbell), U.S. Pat. No. 5,599,695 (Pease), U.S. Pat. No. 5,624,711 (Sundberg), U.S. Pat. No. 5,631,734 (Stern), U.S. Pat. No. 5,795,716 (Chee), U.S. Pat. No. 5,831,070 (Pease), U.S. Pat. No. 5,837,832 (Chee), U.S. Pat. No. 5,856,101 (Hubbell), U.S. Pat. No. 5,858,659 (Sapolsky), U.S. Pat. No. 5,936,324 (Montagu), U.S. Pat. No. 5,968,740 (Fodor), U.S. Pat. No. 5,974,164 (Chee), U.S. Pat. No. 5,981,185 (Matson), U.S. Pat. No. 5,981,956 (Stern), U.S. Pat. No. 6,025,601 (Trulson), U.S. Pat. No. 6,033,860 (Lockhart), U.S. Pat. No. 6,040,193 (Winkler), U.S. Pat. No. 6,090,555 (Fiekowsky), and U.S. Pat. No. 6,410,229 (Lockhart), and U.S. Patent Application Publication No. 20030104411 (Fodor). Each of the above patents and applications is incorporated by reference herein in its entirety.

Microarrays may generally be produced using a variety of techniques, such as mechanical or light directed synthesis methods that incorporate a combination of photolithographic methods and solid phase synthesis methods. Techniques for the synthesis of microarrays using mechanical synthesis methods are described in, for example, U.S. Pat. No. 5,384,261 (Winkler) and U.S. Pat. No. 6,040,193 (Winkler). Although a planar array surface is preferred, the microarray may be fabricated on a surface of virtually any shape, or even on a multiplicity of surfaces. Microarrays may be nucleic acids on beads, gels, polymeric surfaces, fibers such as fiber optics, glass or any other appropriate substrate. See, for example, U.S. Pat. No. 5,708,153 (Dower); U.S. Pat. No. 5,770,358 (Dower); U.S. Pat. No. 5,789,162 (Dower); U.S. Pat. No. 5,800,992 (Fodor); and U.S. Pat. No. 6,040,193 (Winkler), each of which is incorporated by reference herein in its entirety.

Microarrays may be packaged in such a manner as to allow for diagnostic use, or they can be an all-inclusive device. See, for example, U.S. Pat. No. 5,856,174 (Lipshutz) and U.S. Pat. No. 5,922,591 (Anderson), both of which are incorporated by reference herein in their entirety.

Microarrays directed to a variety of purposes are commercially available from Affymetrix (Affymetrix, Santa Clara, Calif.). For instance, these microarrays may be used for genotyping and gene expression monitoring for a variety of eukaryotic and prokaryotic species.

The following examples are provided to better illustrate the claimed invention and are not to be interpreted as limiting the scope of the invention. To the extent that specific materials are mentioned, it is merely for purposes of illustration and is not intended to limit the invention. One skilled in the art may develop equivalent means or reactants without the exercise of inventive capacity and without departing from the scope of the invention. It will be understood that many variations can be made in the procedures herein described while still remaining within the bounds of the present invention. It is the intention of the inventors that such variations are included within the scope of the invention.

EXAMPLES Example 1 Collection and Analysis of Gene Expression Data Using Affymetrix U133A and U133B Microarrays

568 cell samples representing various forms of human lymphoid malignancies were obtained by biopsy using known methods described in the literature. The samples were reviewed by a panel of hematopathologists and classified into the following lymphoma types based on current diagnostic criteria:

-   -   231 diffuse large B cell lymphomas (DLBCL)     -   191 follicular lymphomas (FL)     -   26 Burkitt lymphomas (BL)     -   21 mantle cell lymphoma (MCL)     -   18 follicular hyperplasias (FH)     -   17 small cell lymphocytic lymphomas (SLL)     -   16 mucosa-associated lymphoid tissue lymphomas (MALT)     -   13 splenic lymphomas (Splenic)     -   10 cyclin-D1 negative lymphomas with MCL morphology (CD1negMCL)     -   9 multiple myeloma (Mult_Myeloma)     -   6 lymphoplasmacytic lymphomas (LPC)     -   4 post-transplant lymphoproliferative disorders (PTLD)     -   3 lymphoblastic lymphomas (Lymbl)     -   3 nodal marginal zone lymphomas (NMZ)         The 231 DLBCL samples were subdivided into the following         lymphoma types based on gene expression (see below):     -   88 germinal center B cell-like (GCB)     -   78 activated B cell-like (ABC)     -   33 primary mediastinal B cell lymphoma (PMBL)     -   32 samples for which the subtype could not be determined         (UC_DLBCL)         The 16 MALT samples were subdivided into the following four         group based on tumor origin:     -   9 from the gastric region (MALT_gastric)     -   1 from the salivary gland (MALT_salivary)     -   1 from the lung (MALT_lung)     -   1 from the tonsil (MALT_tonsil)     -   4 of unknown origin (MALT_unk)

Each of the 568 cell samples was given a unique sample ID number consisting of the lymphoma type followed by a unique numerical identifier. For example, “ABC_(—)304” refers to an ABC DLBCL sample numbered 304. Cells were purified and RNA was isolated from the purified cells according to known methods described in the literature.

Aliquots of RNA from each sample were applied to Affymetrix U133A and Affymetrix U133B microarrays according to standard Affymetrix protocol. The U133A and U133B microarrays are divided into probe sets, with each probe set consisting of up to 69 oligonucleotide probes 25 nucleotides in length. Each probe set represents a distinct human gene. Information pertaining to these microarrays is available at the Affymetrix company web site. Each microarray was scanned using an Affymetrix scanner, which records signal intensity for every probe on the microarray. This information can be transformed into summary signal values for each probe set using a number of different algorithms, including MAS 5.0, D-chip (Li 2001), or Bioconductor's RMA algorithms (Irizarry 2003). The images produced by the scanner were evaluated by Affymetrix MAS 5.0 software and stored as tables in .txt format. Since each sample was scanned on both microarrays, there are two .txt files for each sample. Each .txt file was given a unique name consisting of the table number, sample ID number (discussed above), and a letter denoting the microarray used. For example, Table_(—)0588_ABC_(—)304_A.txt is the .txt file for Table 588, which contains data for sample ID number ABC_(—)304 from the U133A array. The data for each sample tested is contained in Tables 3-1138.

The signal value for each probe on the U133A and U133B microarrays was normalized to a target value of 500, and the base-2 log of the normalized values was used for the following analyses. Log-signal values for each probe set are presented in Tables 1139-1706, contained in files with the title format “Table_No._NAME_log_signal.txt,” where NAME refers to the sample ID number (e.g., ABC_(—)304). The first column provides the UNIQID for the probe set, while the second column provides the log-signal value.

Log-signal files were statistically analyzed using S+ software and the S+ subtype predictor script contained in the file entitled “Subtype_Predictor.txt,” located in the computer program listing appendix contained on CD number 22 of 22. Although the log-signal values were analyzed using S+ software and the above algorithm, any effective software/algorithm combination may be used. Tables 1707-1721 provide descriptive statistical characteristics for each of the lymphoma types tested except for CD1negMCL, non-gastric MALT, and UC_DLBCL. Table 1722 provides statistical characteristics for all MALT samples combined, while Table 1723 does likewise for all DLBCL samples.

The files containing Tables 1707-1723 have the title format “Table_No._TYPE_Stats.txt,” where TYPE refers to the lymphoma type. Each row of these tables represents a particular probe set. The first column of each table provides the UNIQID for the probe set, while the second column provides the average log-signal for the probe set over all samples of a particular lymphoma type. The third column provides the log-fold change in expression of the probe set between the lymphoma type in question and a second lymphoma type. For example, if logfold.ABC.vs.GCB is −0.21 for gene X, expression of gene X in the ABC DLBCL samples is, on average, 0.86 (i.e., 2^(−0.21)) times greater than expression of gene X in the GCB DLBCL samples. The fourth column provides a two-sided P-value derived from a t-test of the log signals of the two lymphoma types compared in column three. If, for example, P.value.ABC.vs.GCB was 0.00001 for gene X, this would indicate that the observed difference in expression of gene X between ABC DLBCL and GCB DLBCL would only occur approximately one time in 100,000 if there was no actual difference in gene X expression between the two lymphoma types. The remainder of the columns can be read as pairs that repeat the pattern of columns three and four, presenting the log-fold change and P-value of the difference in expression of the probe set for the lymphoma type in question versus all other lymphoma types being tested. Tables 1710, 1715, and 1723 (corresponding to FL, MCL, and DLBCL, respectively) contain two additional columns entitled “TYPE_Cox coefficient” and “TYPE_Cox_P_value.” The content of these columns is discussed in the following examples.

Example 2 Collection of Gene Expression Data Using the Novel Lymph Dx Microarray

The novel Lymph Dx microarray contains cDNA probes corresponding to approximately 2,734 genes. 174 of these are “housekeeping” genes present for quality control, since they represent genes that are most variably expressed across all lymphoma samples. Other genes represented on the microarray were selected for their utility in identifying particular lymphoma samples and predicting survival in those samples. The genes represented on the Lymph Dx microarray can be divided into four broad categories: 1,101 lymphoma predictor genes identified previously using the Affymetrix U133 microarray, 171 outcome predictor genes identified using the Affymetrix U133 microarray, 167 genes not found on the Affymetrix U133 microarray but represented on the Lymphochip microarray (Alizadeh 1999), and 1,121 named genes. The types of genes making up each of these broad categories are summarized in Table 1724, below, while the specific genes represented on the Lymph Dx microarray are listed in Table 2, contained in the file “Table_(—)0002_LymphDx_Probe_List.txt.”

TABLE 1724 Number of Gene type genes Lymphoma predictor genes 1101 Subtype specific 763 Lymph node signature 178 Proliferation signature 160 Outcome predictor genes 171 DLBCL 79 FL 81 MCL 11 New genes not on U133 167 Lymphochip lymphoma predictor genes 84 EBV and HHV8 viral genes 18 BCL-2/cyclin D1/INK4a specialty probes 14 Named genes missing from U133 51 Named genes 1121 Protein kinase 440 Interleukin 35 Interleukin receptor 29 Chemokine 51 Chemokine receptor 29 TNF family 26 TNF receptor family 51 Adhesion 45 Surface marker 264 Oncogene/tumor suppressor 49 Apoptosis 46 Drug target 10 Regulatory 46

Cell samples representing various forms of human lymphoid malignancy were obtained by biopsy using known methods described in the literature. These 634 biopsy samples were reviewed by a panel of hematopathologists and classified into the following lymphoma types based on current diagnostic criteria:

-   -   201 diffuse large B-cell lymphomas (DLBCL)     -   191 follicular lymphomas (FL)     -   60 Burkitt lymphomas (BL)     -   21 mantle cell lymphomas (MCL)     -   30 primary mediastinal B cell lymphoma (PMBL)     -   18 follicular hyperplasias (FH)     -   18 small cell lymphocytic lymphomas (SLL)     -   17 mucosa-associated lymphoid tissue lymphomas (MALT), including         9 gastric MALTs (GMALT)     -   16 chronic lymphocytic leukemias (CLL)     -   13 splenic lymphomas (SPL)     -   11 lymphoplasmacytic lymphomas (LPC)     -   11 transformed DLBCL (trDLBCL) (DLBCL that arose from an         antecedent FL)     -   10 cyclin D1 negative lymphomas with MCL morphology (CD1N)     -   6 peripheral T-cell lymphoma (PTCL)     -   4 post-transplant lymphoproliferative disorders (PTLD)     -   4 nodal marginal zone lymphomas (NMZ)     -   3 lymphoblastic lymphomas (LBL)

Each of the 634 samples was given a unique sample ID number consisting of the lymphoma type followed by a unique numerical identifier. For example, “BL_(—)2032_(—)52748” refers to a Burkitt lymphoma sample with the numerical identifier 2032_(—)52748. Cells were purified and RNA was isolated from the purified cells according to known methods described in the literature.

Aliquots of purified RNA from each sample was applied to the Lymph Dx microarrays according to standard Affymetrix microarray protocol. Each microarray was scanned on an Affymetrix scanner. This scanner produced an image of the microarray, which was then evaluated by Affymetrix MAS 5.0 software. This information was stored in tables in .txt format. Each of these .txt files was given a unique name consisting of the table number, the sample ID number (discussed above), and the UNIQID for identifying the array data in the National Cancer Institute Database. For example, Table_(—)1725_BL_(—)2032_(—)52748.txt is the .txt file for Table 1725, which contains data for sample ID number BL_(—)2032. The data for each sample analyzed is contained in Tables 1725-2358. The signal intensity for each probe on the microarray can be transformed into summary signal values for each probe set through a number of different algorithms, including but not limited to MAS 5.0, D-chip (Li 2001), or Bioconductor's RMA algorithms (Irizarry 2003).

Example 3 Development of a First FL Survival Predictor Using Gene Expression Data from Affymetrix U133A and U133B Microarrays

An analytical method entitled Survival Signature Analysis was developed to create survival prediction models for lymphoma. This method is summarized in FIG. 2. The key feature of this method is the identification of gene expression signatures. Survival Signature Analysis begins by identifying genes whose expression patterns are statistically associated with survival. A hierarchical clustering algorithm is then used to identify subsets of these genes with correlated expression patterns across the lymphoma samples. These subsets are operationally defined as “survival-associated signatures.” Evaluating a limited number of survival-associated signatures mitigates the multiple comparison problems that are inherent in the use of large-scale gene expression data sets to create statistical models of survival (Ransohoff 2004).

FL samples were divided into two equivalent groups: a training set (95 samples) for developing the survival prediction model, and a validation set (96 samples) for evaluating the reproducibility of the model. The overall survival of this cohort is depicted in FIG. 3. The median age at diagnosis was 51 years (ranging from 23 to 81 years), and the patients had a median follow-up of 6.6 years (8.1 years for survivors, with a range of <1 to 28.2 years). Gene expression data from Affymetrix U133A and U133B microarrays was obtained for each sample. Within the training set, a Cox proportional hazards model was used to identify “survival predictor” genes, which were genes whose expression levels were associated with long survival (good prognosis genes) or short survival (poor prognosis genes). A hierarchical clustering algorithm (Eisen 1998) was used to identify gene expression signatures within the good and poor prognosis genes according to their expression pattern across all samples. Ten gene expression signatures were observed within either the good prognosis or poor prognosis gene sets (FIG. 4). The expression level of every component gene in each of these ten gene expression signatures was averaged to create a gene expression signature value.

To create a multivariate model of survival, different combinations of the ten gene expression signature values were generated and evaluated for their ability to predict survival within the training set. Among models consisting of two signatures, an exceptionally strong statistical synergy was observed between one signature from the good prognosis group and one signature from the poor prognosis group. These signatures were deemed “immune response-1” and “immune response-2,” respectively; based on the biological function of certain genes within each signature. The immune response-1 gene expression signature included genes encoding T cell markers (e.g., CD7, CD8B1, ITK, LEF1, STAT4) and .genes that are highly expressed in macrophaqes (e.g., ACTN1, TNFSF13B). The immune response-1 signature is not merely a surrogate for the number of T cells in the FL biopsy sample because many other standard T cell genes (e.g., CD2, CD4, LAT, TRIM, SH2D1A) were not associated with survival. The immune response-2 gene expression signature included genes known to be preferentially expressed in macrophages and/or dendritic cells (e.g., TLR5, FCGR1A, SEPT10, LGMN, C3AR1). Table 2359 lists the genes that were used to generate the gene expression signature values for the immune response-1 and immune response-2 signatures. The Unigene ID Build database referenced in the following tables is hosted by the hosted by the National Center for Biotechnology Information (NCBI) web site.

TABLE 2359 Signature UNIQID Unigene ID Build 167 Gene symbol Immune response-1 1095985 83883 TMEPAI Immune response-1 1096579 117339 HCST Immune response-1 1097255 380144 Immune response-1 1097307 379754 LOC340061 Immune response-1 1097329 528675 TEAD1 Immune response-1 1097561 19221 C20orf112 Immune response-1 1098152 377588 KIAA1450 Immune response-1 1098405 362807 IL7R Immune response-1 1098548 436639 NFIC Immune response-1 1098893 43577 ATP8B2 Immune response-1 1099053 376041 Immune response-1 1100871 48353 Immune response-1 1101004 2969 SKI Immune response-1 1103303 49605 C9orf52 Immune response-1 1107713 171806 Immune response-1 1115194 270737 TNFSF13B Immune response-1 1119251 433941 SEPW1 Immune response-1 1119838 469951 GNAQ Immune response-1 1119924 32309 INPP1 Immune response-1 1120196 173802 TBC1D4 Immune response-1 1120267 256278 TNFRSF1B Immune response-1 1121313 290432 HOXB2 Immune response-1 1121406 NA TNFSF12 Immune response-1 1121720 80642 STAT4 Immune response-1 1122956 113987 LGALS2 Immune response-1 1123038 119000 ACTN1 Immune response-1 1123092 437191 PTRF Immune response-1 1123875 428 FLT3LG Immune response-1 1124760 419149 JAM3 Immune response-1 1128356 415792 C1RL Immune response-1 1128395 7188 SEMA4C Immune response-1 1132104 173802 TBC1D4 Immune response-1 1133408 12802 DDEF2 Immune response-1 1134069 405667 CD8B1 Immune response-1 1134751 106185 RALGDS Immune response-1 1134945 81897 KIAA1128 Immune response-1 1135743 299558 TNFRSF25 Immune response-1 1135968 119000 ACTN1 Immune response-1 1136048 299558 TNFRSF25 Immune response-1 1136087 211576 ITK Immune response-1 1137137 195464 FLNA Immune response-1 1137289 36972 CD7 Immune response-1 1137534 36972 CD7 Immune response-1 1139339 47099 GALNT12 Immune response-1 1139461 14770 BIN2 Immune response-1 1140391 44865 LEF1 Immune response-1 1140524 10784 C6orf37 Immune response-1 1140759 298530 RAB27A Immune response-2 1118755 127826 EPOR Immune response-2 1118966 19196 LOC51619 Immune response-2 1121053 1690 FGFBP1 Immune response-2 1121267 334629 SLN Immune response-2 1121331 8980 TESK2 Immune response-2 1121766 396566 MPP3 Immune response-2 1121852 421391 LECT1 Immune response-2 1122624 126378 ABCG4 Immune response-2 1122679 232770 ALOXE3 Immune response-2 1122770 66578 CRHR2 Immune response-2 1123767 1309 CD1A Immune response-2 1123841 389 ADH7 Immune response-2 1126097 498015 Immune response-2 1126380 159408 Immune response-2 1126628 254321 CTNNA1 Immune response-2 1126836 414410 NEK1 Immune response-2 1127277 121494 SPAM1 Immune response-2 1127519 NA Immune response-2 1127648 285050 Immune response-2 1128483 444359 SEMA4G Immune response-2 1128818 115830 HS3ST2 Immune response-2 1129012 95497 SLC2A9 Immune response-2 1129582 272236 C21orf77 Immune response-2 1129658 58356 PGLYRP4 Immune response-2 1129705 289368 ADAM19 Immune response-2 1129867 283963 G6PC2 Immune response-2 1130003 432799 Immune response-2 1130388 19196 LOC51619 Immune response-2 1131837 156114 PTPNS1 Immune response-2 1133843 6682 SLC7A11 Immune response-2 1133949 502092 PSG9 Immune response-2 1134447 417628 CRHR1 Immune response-2 1135117 512646 PSG6 Immune response-2 1136017 1645 CYP4A11 Immune response-2 1137478 315235 ALDOB Immune response-2 1137745 26776 NTRK3 Immune response-2 1137768 479985 Immune response-2 1138476 351874 HLA-DOA Immune response-2 1138529 407604 CRSP2 Immune response-2 1138601 149473 PRSS7 Immune response-2 1139862 251383 CHST4 Immune response-2 1140189 287369 IL22 Immune response-2 1140389 22116 CDC14B

Although the immune response-1 and immune response-2 gene expression signatures taken individually were not ideal predictors of survival, the binary model formed by combining the two was more predictive of survival in the training set than any other binary model (p<0.001). Using this binary model as an anchor, other signatures were added to the model using a step up procedure (Drapner 1966). Of the remaining eight signatures, only one signature contributed significantly to the model in the training set (p<0.01), resulting in a three-variable model for survival. This model was associated with survival in a highly statistically significant fashion in both the training (p<0.001) and validation sets (p=0.003). However, only the immune response-1 and immune response-2 gene expression signatures contributed to the predictive power of the model in both the training set and the validation set. The predictive power of each of these signatures is summarized in Table 2360.

TABLE 2360 Contribution of Relative signature risk of death Effect of to model among patients increased Gene expression in validation set in validation expression signature (p-value) set (95% C.I.) on survival Immune response-1 <0.001 0.15 (0.05-0.46) Favorable Immune response-2 <0.001 9.35 (3.02-28.9) Poor Based on this information, the third signature was removed from the model and the two-signature model was used to generate a survival predictor score using the following equation: Survival predictor score=[(2.71*immune response-2 gene expression signature value)]−[(2.36×immune response-1 gene expression signature value)]. A higher survival predictor score was associated with worse outcome. The two-signature model was associated with survival in a statistically significant fashion in both the training set (p<0.001) and the validation set (p<0.001), which demonstrated that the model was reproducible. For the 187 FL samples with available clinical data, the survival predictor score had a mean of 1.6 and a standard deviation of 0.894, with each unit increase in the predictor score corresponding to a 2.5 fold increase in the relative risk of death. Data for all 191 samples is shown in Table 2361.

TABLE 2361 Length of Status Immune Immune Survival Sample follow-up at response-1 response-2 predictor ID # Set (years) follow-up signature value signature value score FL_1073 Training 7.68 Dead 9.20 8.67 1.77 FL_1074 Training 4.52 Dead 9.10 8.57 1.74 FL_1075 Validation 4.52 Dead 8.97 8.69 2.38 FL_1076 Training 3.22 Dead 9.20 8.55 1.44 FL_1077 Training 7.06 Alive 9.80 8.46 −0.20 FL_1078 Training 4.95 Alive 9.32 8.23 0.30 FL_1080 Training 6.05 Alive 9.45 8.94 1.93 FL_1081 Validation 6.61 Alive 9.00 8.22 1.05 FL_1083 Training 10.01 Alive 9.82 8.72 0.47 FL_1085 Validation 8.84 Alive 9.31 8.58 1.29 FL_1086 Validation 1.98 Dead 9.49 9.09 2.22 FL_1087 Training 8.19 Alive 9.98 9.27 1.57 FL_1088 Validation 5.30 Alive 9.22 8.47 1.20 FL_1089 Training 10.72 Alive 9.42 8.35 0.40 FL_1090 Validation 10.20 Alive 9.27 8.37 0.82 FL_1097 Validation 8.79 Dead 9.87 8.92 0.87 FL_1098 Validation 5.34 Dead 9.33 8.81 1.87 FL_1099 Training 7.65 Alive 9.73 9.04 1.54 FL_1102 Validation 13.20 Dead 9.45 8.89 1.79 FL_1104 Training 8.42 Dead 9.30 8.27 0.48 FL_1106 Validation 7.94 Alive 9.13 9.19 3.36 FL_1107 Training 5.01 Dead 9.41 9.32 3.07 FL_1183 Training 11.56 Dead 9.31 8.53 1.16 FL_1184 Training 6.93 Dead 9.66 8.83 1.13 FL_1185 Validation 7.02 Dead 9.23 9.09 2.86 FL_1186 Training 1.34 Dead 9.01 8.84 2.68 FL_1416 Validation 6.21 Alive 9.50 8.67 1.08 FL_1417 Training 2.40 Dead 8.47 8.39 2.73 FL_1418 Validation 3.59 Alive 8.94 8.42 1.72 FL_1419 Training 3.85 Alive 9.82 8.56 0.03 FL_1422 Training 5.72 Alive 9.46 8.49 0.68 FL_1425 Validation 4.26 Alive 8.93 8.50 1.98 FL_1426 Training 7.32 Alive 9.08 8.26 0.97 FL_1427 Training 5.22 Alive 8.57 8.28 2.22 FL_1428 Validation 5.41 Dead 9.22 8.44 1.10 FL_1432 Training 3.66 Alive 9.22 8.95 2.51 FL_1436 Training 9.08 Dead 9.48 8.63 1.02 FL_1440 Training 7.85 Alive 9.07 8.35 1.22 FL_1445 Training 9.24 Dead 8.67 8.66 3.01 FL_1450 Validation 0.65 Dead 9.83 9.99 3.86 FL_1472 Validation 16.72 Alive 8.85 8.49 2.10 FL_1473 Training 15.07 Alive 9.75 8.50 0.02 FL_1474 Validation 2.75 Dead 9.34 9.10 2.62 FL_1476 Validation 4.08 Dead 9.51 8.87 1.60 FL_1477 Training 0.59 Dead 9.64 9.06 1.83 FL_1478 Training 12.47 Dead 9.60 8.87 1.39 FL_1479 Training 2.29 Dead 8.71 9.07 4.01 FL_1480 Training 16.29 Alive 9.40 8.67 1.30 FL_1579 Training 8.22 Dead 8.81 8.44 2.10 FL_1580 Training 19.30 Alive 9.58 8.52 0.49 FL_1581 Training 9.52 Dead 9.08 9.02 3.00 FL_1582 Validation 1.30 Dead 8.40 8.18 2.36 FL_1583 Training 15.26 Dead 9.47 8.79 1.48 FL_1584 Training 15.73 Dead 9.44 8.55 0.89 FL_1585 Validation 0.01 Alive 8.96 8.53 1.96 FL_1586 Validation 3.11 Alive 9.38 8.55 1.03 FL_1588 Training 0.49 Dead 9.52 9.06 2.08 FL_1589 Training 3.15 Alive 9.72 8.74 0.72 FL_1591 Training 11.22 Alive 9.49 8.62 0.97 FL_1594 Validation 11.19 Alive 9.25 8.59 1.47 FL_1595 Training 8.03 Alive 9.75 9.60 3.01 FL_1598 Validation 2.80 Dead 8.81 8.33 1.79 FL_1599 Validation 6.17 Alive 9.48 8.65 1.06 FL_1603 Training 5.17 Dead 9.66 9.75 3.63 FL_1604 Training 3.98 Dead 9.24 8.86 2.20 FL_1606 Validation 4.22 Dead 9.45 9.18 2.57 FL_1607 Validation 8.12 Alive 9.40 8.60 1.13 FL_1608 Validation 9.70 Alive 8.92 8.41 1.72 FL_1610 Validation 2.05 Dead 9.33 9.35 3.32 FL_1611 Validation 10.15 Alive 9.42 8.69 1.31 FL_1616 Training 2.36 Dead 9.38 8.82 1.78 FL_1617 Validation 7.85 Alive 8.96 8.49 1.87 FL_1619 Validation 9.24 Dead 9.43 8.56 0.94 FL_1620 Validation 9.36 Dead 9.14 8.35 1.04 FL_1622 Training 14.01 Alive 9.23 8.53 1.33 FL_1623 Training 9.72 Alive 9.67 8.93 1.38 FL_1624 Validation 3.98 Dead 9.05 8.50 1.70 FL_1625 Validation 11.16 Alive 8.98 8.47 1.75 FL_1626 Validation 6.47 Dead 8.59 8.14 1.76 FL_1628 Validation 0.82 Dead 9.80 8.72 0.51 FL_1637 Validation 18.81 Alive 9.95 9.58 2.48 FL_1638 Validation 4.06 Alive 9.13 8.88 2.51 FL_1639 Training 4.75 Alive 9.53 8.89 1.62 FL_1643 Training 0.77 Dead 9.73 9.06 1.58 FL_1644 Validation 3.84 Alive 9.55 8.68 0.98 FL_1645 Training 3.56 Alive 9.49 8.70 1.18 FL_1646 Training 1.97 Dead 9.25 8.61 1.50 FL_1647 Training 1.22 Dead 9.12 8.89 2.55 FL_1648 Training 11.01 Alive 9.13 8.12 0.46 FL_1652 Training 3.72 Dead 9.50 9.14 2.35 FL_1654 Validation 0.30 Dead 8.74 8.28 1.82 FL_1655 Training 8.45 Alive 9.51 8.85 1.53 FL_1656 Validation 9.36 Alive 9.06 8.58 1.87 FL_1657 Training 10.09 Alive 9.53 8.46 0.44 FL_1660 Training 2.32 Alive 8.81 8.38 1.91 FL_1661 Validation 1.48 Alive 9.86 8.90 0.85 FL_1662 Validation 0.74 Dead 9.57 9.15 2.21 FL_1664 Validation 4.53 Dead 9.34 8.62 1.31 FL_1669 Training 4.40 Dead 8.87 8.58 2.30 FL_1670 Training 1.88 Alive 9.64 9.45 2.86 FL_1675 Training 4.57 Alive 9.36 8.46 0.84 FL_1681 Validation 4.23 Alive 9.52 8.63 0.91 FL_1683 Validation 4.03 Dead 9.95 9.10 1.19 FL_1684 Training 2.88 Dead 9.53 8.73 1.18 FL_1716 Validation 9.69 Alive 8.95 8.35 1.50 FL_1717 Validation 2.01 Dead 9.35 8.88 1.98 FL_1718 Training 10.35 Alive 9.23 8.13 0.26 FL_1719 Validation 7.70 Dead 9.13 8.50 1.49 FL_1720 Training 3.91 Dead 8.78 8.88 3.33 FL_1729 Training 8.06 Alive 9.35 8.65 1.39 FL_1732 Validation 0.71 Dead 7.81 8.59 4.86 FL_1761 Validation 10.83 Alive 9.31 8.55 1.22 FL_1764 Training 0.42 Dead 9.25 8.87 2.21 FL_1768 Training 13.04 Alive 9.42 8.47 0.72 FL_1771 Training 9.26 Dead 9.09 8.67 2.06 FL_1772 Validation 13.64 Dead 9.49 8.49 0.61 FL_1788 Training 1.00 Dead 9.09 9.13 3.29 FL_1790 Training 1.42 Alive 9.85 9.40 2.22 FL_1792 Validation 2.01 Dead 9.33 8.72 1.61 FL_1795 Training 0.71 Dead 10.19 9.27 1.08 FL_1797 Validation 7.17 Alive 9.34 8.92 2.14 FL_1799 Training 14.18 Alive 9.32 8.63 1.38 FL_1810 Validation 9.91 Alive 8.66 8.41 2.35 FL_1811 Validation 3.04 Alive 9.38 8.27 0.29 FL_1825 Training 2.98 Alive 9.46 9.07 2.25 FL_1827 Training 3.66 Alive 9.80 8.84 0.83 FL_1828 Validation 11.51 Alive 8.99 8.09 0.72 FL_1829 Validation 4.11 Alive 9.57 8.73 1.08 FL_1830 Validation 5.65 Dead 9.01 8.68 2.25 FL_1833 Training 11.95 Alive 9.74 8.67 0.51 FL_1834 Validation 15.92 Alive 9.22 8.72 1.88 FL_1835 Validation 12.49 Alive 9.26 8.83 2.10 FL_1836 Validation 12.24 Alive 9.55 8.64 0.85 FL_1837 Validation 0.55 Dead 9.47 8.84 1.62 FL_1838 Validation 2.54 Alive 9.90 9.12 1.34 FL_1839 Training 4.48 Alive 8.56 8.32 2.34 FL_1841 Training 0.88 Dead 9.32 9.10 2.66 FL_1842 Validation 4.56 Alive 9.73 8.87 1.07 FL_1844 Validation 13.39 Alive 9.41 8.55 0.98 FL_1845 Training 12.92 Dead 9.89 9.04 1.16 FL_1846 Validation 1.80 Dead 9.79 9.61 2.93 FL_1848 Training 12.52 Alive 9.76 8.81 0.82 FL_1851 Training 4.08 Dead 9.43 9.01 2.18 FL_1853 Validation 12.50 Alive 9.28 8.54 1.25 FL_1854 Validation 13.81 Alive 9.32 8.84 1.98 FL_1855 Validation 9.96 Dead 9.31 8.39 0.75 FL_1857 Validation 8.39 Dead 9.80 9.14 1.65 FL_1861 Validation 3.19 Dead 9.47 8.57 0.88 FL_1862 Validation 7.22 Dead 8.96 8.33 1.44 FL_1863 Validation 10.77 Dead 9.31 8.85 2.00 FL_1864 Training 14.25 Alive 9.98 9.12 1.17 FL_1866 Training 10.72 Dead 9.93 8.94 0.79 FL_1870 Validation 6.41 Dead 10.01 9.22 1.36 FL_1873 Training 7.78 Dead 9.39 8.66 1.30 FL_1874 Validation 3.15 Dead 9.38 8.74 1.53 FL_1876 Validation 15.07 Alive 9.59 8.72 0.98 FL_1879 Training 7.13 Dead 9.25 8.62 1.53 FL_1880 Validation 12.84 Dead 8.82 8.35 1.82 FL_1882 Training 8.84 Dead 9.43 8.76 1.49 FL_1884 Validation 11.92 Dead 9.48 9.14 2.41 FL_1885 Validation 15.49 Alive 9.70 8.85 1.11 FL_1887 Training 5.14 Dead 9.47 8.57 0.87 FL_1888 Training 15.08 Alive 9.83 8.97 1.11 FL_1890 Training 3.03 Dead 9.29 9.05 2.60 FL_1894 Training 11.37 Dead 9.01 8.64 2.13 FL_1896 Training 12.03 Alive 9.80 8.56 0.08 FL_1897 Training 9.63 Alive 9.02 8.33 1.29 FL_1898 Training 5.20 Alive 8.82 8.25 1.54 FL_1900 Validation 7.38 Alive 9.13 8.26 0.85 FL_1903 Validation 28.25 Alive 9.07 8.46 1.54 FL_1904 Validation 7.36 Alive 9.16 8.53 1.50 FL_1905 Validation 3.68 Dead 9.25 8.38 0.87 FL_1906 Training 2.35 Dead 8.04 8.69 4.56 FL_1907 Validation 2.35 Dead 8.11 8.21 3.11 FL_1910 Training 13.84 Alive 9.36 8.72 1.56 FL_1912 Validation 0.73 Dead 9.30 9.21 3.02 FL_1913 Training 2.57 Alive 9.77 8.51 0.01 FL_1916 Validation 11.61 Alive 9.22 8.49 1.24 FL_1918 Validation 9.95 Dead 9.54 8.77 1.26 FL_1919 Training 10.84 Dead 9.51 8.81 1.44 FL_735 Validation 11.05 Dead 8.81 8.23 1.53 FL_738 Validation 10.15 Dead 9.19 8.79 2.13 FL_739 Training 10.80 Dead 9.29 8.77 1.85 FL_878 Validation 3.87 Dead 8.85 8.54 2.26 FL_879 Training 4.34 Dead 8.95 8.74 2.56 FL_886 Validation 3.29 Alive 9.43 8.72 1.40 FL_888 Validation 1.32 Dead 8.76 8.49 2.34 FL_1627 Training NA NA 9.60 8.51 0.40 FL_1429 Training NA NA 8.69 8.28 1.93 FL_1850 Validation NA NA 9.75 8.83 0.92 FL_1735 Validation NA NA 7.32 8.30 5.24

In order to visualize the predictive power of the model, the FL samples were ranked according to their survival predictor scores and divided into four quartiles. Kaplan-Meier plots of overall survival showed clear differences in survival rate in the validation set (FIG. 5). The median survival for each of the four quartiles is set forth in Table 2362.

TABLE 2362 Quartile Median survival (years) 1 13.6 2 11.1 3 10.8 4 3.9

Various clinical variables were found to be significantly associated with survival, including the IPI and some of its components and the presence of B-symptoms. The gene expression-based model was independent of each of these variables at predicting survival. These clinical variables and the relative risk of death associated with each are summarized in Table 2363.

TABLE 2363 Multivariate (clinical Univariate (clinical variable + survival variable only) predictor score) relative risk of death relative risk of death % of % of among patients in among patients in patients¹ patients¹ validation set validation set Clinical Training Validation RR² (95% RR² (95% variable Criteria set set C.I.) p-value C.I.) p-value Age   60 64.5 70.2 1.90 0.044 2.21 (1.48-3.29) <0.001 >60 35.5 29.8 (1.02-3.56) Stage I-II 33.3 25 1.31 0.447 2.31 (1.51-3.52) <0.001 III-IV 66.7 75 (0.65-2.64) Extranodal    2 5.4 20.2 1.58 0.163 2.21 (1.48-3.30) <0.001 sites (#)  <2 94.6 79.8 (0.83-2.99) LDH Normal 77.1 66.2 1.77 0.065 2.40 (1.57-3.67) <0.001 Greater 22.9 33.8 (0.97-3.24) than normal ECOG    2 9.4 12.5 2.05 0.090 2.17 (1.40-3.35) <0.001 performance  <2 90.6 87.5 (0.89-4.71) status Gender Male 42 65 1.62 0.105 2.17 (1.45-3.25) <0.001 Female 58 35 (0.90-2.90) B-symptoms Present 17.2 21.3 2.05 0.029 2.10 (1.37-3.23) <0.001 Absent 82.8 78.7 (1.08-3.89) Grade³    1 45 43.4 N/A 0.118 2.55 (1.63-3.99) <0.001    2 34.8 33.3 2.03 (1.04-3.96)    3 20.2 23.3 1.39 (0.65-2.98) Int'l. Scores 63.1 47.5 N/A 0.029 2.28 (1.46-3.57) <0.001 Prognostic 0-1 Index⁴ Scores 33.3 45 2.07 2-3 (1.07-4.00) Scores 3.6 7.5 3.73 4-5  (1.18-11.18) ¹Due to rounding, percentages may not total 100 ²Relative risk of death (RR) based on 2-fold increase in expression ³RR for grades 2 and 3 calculated with respect to risk of death for grade 1. The p-value is calculated for all grades. ⁴RR for scores 2-3 and 4-5 calculated with respect to risk of death for scores 0-1. The p-value is calculated for all grades.

The samples in the validation set were divided into three groups based on their IPI score, and the relationship between survival and IPI score was visualized by Kaplan-Meier plot (FIG. 6). Among validation set samples from the low-risk (IPI 0-1) and intermediate risk (IPI 2-3) IPI groups, the gene-expression-based survival predictor could stratify patients into groups differing by more than 5 years with regards to median survival (FIG. 7). The high-risk IPI group (IPI 4-5) comprised less than 5% of the samples, and was omitted from this analysis. These results demonstrate that the gene expression-based model is not merely acting as a surrogate for clinical variables that are known to predict survival in FL, but rather it identifies distinct biological attributes of the tumors that are associated with survival.

Example 4 Development of a Second FL Survival Predictor Using Gene Expression Data from Affymetrix U133A and U133B Microarrays

191 FL were divided into two equivalent groups: a training set (95 samples) for developing the survival prediction model, and a validation set (96 samples) for evaluating the reproducibility of the model. Gene expression data from Affymetrix U133A and U133B microarrays was obtained for each of the samples. A Cox proportional hazards model was used to identify survival predictor genes whose expression levels were associated with long survival (good prognosis genes) or short survival (poor prognosis genes) in the training set. The correlation between expression and survival for each gene on the microarrays is provided in the final two columns of Table 1710. The first of these two columns (“FL_Cox_coefficient”) provides a Cox coefficient indicating the extent to which a 2-fold increase in expression of a particular gene affects mortality. A positive Cox coefficient indicates increasing mortality with increasing expression of the gene, while a negative Cox coefficient indicates decreasing mortality with increasing expression of the gene. The second of these two columns provides a Cox p-value indicating the estimated probability that the increase or decrease in survival associated with the gene would occur by chance if there was no connection between the expression of the gene and survival.

A hierarchical clustering algorithm (Eisen 1998) was used to identify gene expression signatures within the good and poor prognosis genes according to their expression pattern across all samples. Eight clusters of coordinately regulated genes were observed within the good prognosis gene set and six clusters were observed in the poor prognosis gene sets. The expression level of every component gene in each of these gene expression signatures was averaged to create a gene expression signature value. After averaging, only ten of the gene expression signatures were found to be significantly associated with survival in the training set (p<0.01). To create a multivariate model of survival, different combinations of these ten gene expression signature averages were generated and evaluated for their ability to predict survival within the training set. Among models consisting of two signatures, an exceptionally strong statistical synergy was noted between one signature from the good prognosis group and one from the poor prognosis group. These gene expression signatures were termed “T-cell” and “macrophage” based on the biological function of certain genes within each signature. The T-cell gene expression signature included genes that were typically expressed in T-cells, while the macrophage gene expression signature included a number of genes typically expressed in macrophages. Although these two signatures taken individually were not the best predictors of survival, the binary model formed by combining the two was more predictive than any combination of three signatures that did not contain these two signatures. Using these two signatures as an anchor, other signatures were added to the model using a step up procedure (Drapner 1966). Only one of the remaining eight signatures, termed the B-cell differentiation signature, contributed significantly to the model in the training set (p=0.054). The B-cell differentiation signature included a number of genes that appear to be involved in B-cell signal transduction. Table 2364 lists the genes that were used to generate the gene expression signature values for the T-cell, macrophage, and B-cell differentiation gene expression signatures.

TABLE 2364 Signature UNIQID Unigene ID Build 167 Gene symbol B-cell differentiation 1119350 331141 ALDH2 B-cell differentiation 1130922 459987 ANP32B B-cell differentiation 1130923 459987 ANP32B B-cell differentiation 1099291 130774 C9orf105 B-cell differentiation 1102859 446195 FLJ42418 B-cell differentiation 1120976 245644 GCHFR B-cell differentiation 1098862 303669 MGC26694 B-cell differentiation 1111070 202201 B-cell differentiation 1105935 B-cell differentiation 1139017 274424 NANS B-cell differentiation 1108988 3532 NLK B-cell differentiation 1114726 3532 NLK B-cell differentiation 1097897 266175 PAG B-cell differentiation 1097901 266175 PAG B-cell differentiation 1119813 155342 PRKCD B-cell differentiation 1123298 20191 SIAH2 B-cell differentiation 1101439 63335 TERF2 B-cell differentiation 1120316 63335 TERF2 B-cell differentiation 1096035 105794 UGCGL1 T-cell 1134945 81897 KIAA1128 T-cell 1134069 405667 CD8B1 T-cell 1137809 405667 CD8B1 T-cell 1119251 433941 SEPW1 T-cell 1096579 117339 HCST T-cell 1101004 2969 SKI T-cell 1137137 195464 FLNA T-cell 1100871 48353 T-cell 1139461 14770 BIN2 T-cell 1128395 7188 SEMA4C T-cell 1119880 442844 FMOD T-cell 1130676 194431 KIAA0992 T-cell 1130668 194431 KIAA0992 T-cell 1135968 119000 ACTN1 T-cell 1097329 528675 TEAD1 T-cell 1098548 436639 NFIC T-cell 1123038 119000 ACTN1 T-cell 1128356 415792 C1RL T-cell 1133408 12802 DDEF2 T-cell 1140524 10784 C6orf37 T-cell 1119838 469951 GNAQ T-cell 1097255 380144 T-cell 1098152 377588 KIAA1450 T-cell 1115194 270737 TNFSF13B T-cell 1124760 419149 JAM3 T-cell 1120267 256278 TNFRSF1B T-cell 1137289 36972 CD7 T-cell 1137534 36972 CD7 T-cell 1097307 379754 LOC340061 T-cell 1123613 97087 CD3Z T-cell 1121720 80642 STAT4 T-cell 1120196 173802 TBC1D4 T-cell 1136087 211576 ITK T-cell 1132104 173802 TBC1D4 T-cell 1140391 44865 LEF1 T-cell 1098405 362807 IL7R T-cell 1135743 299558 TNFRSF25 T-cell 1136048 299558 TNFRSF25 T-cell 1123875 428 FLT3LG T-cell 1098893 43577 ATP8B2 T-cell 1097561 19221 C20orf112 T-cell 1122956 113987 LGALS2 T-cell 1121406 TNFSF12 T-cell 1125532 T-cell 1138538 2014 TRD T-cell 1103303 49605 C9orf52 T-cell 1119924 32309 INPP1 Macrophage 1123682 114408 TLR5 Macrophage 1099124 355455 SEPT10 Macrophage 1123401 50130 NDN Macrophage 1134379 150833 C4A Macrophage 1137481 150833 C4A Macrophage 1132220 448805 GPRC5B Macrophage 1119400 181046 DUSP3 Macrophage 1131119 349656 SCARB2 Macrophage 1123566 155935 C3AR1 Macrophage 1138443 77424 FCGR1A Macrophage 1127943 9641 C1QA Macrophage 1119998 8986 C1QB Macrophage 1132433 14732 ME1 Macrophage 1119260 18069 LGMN Macrophage 1098278 166017 MITF The three signatures were used to generate a survival predictor score using the following equation: Survival predictor score=[2.053*(macrophage gene expression signature value)]−[2.344*(T-cell gene expression signature value)]−[0.729*(B-cell differentiation gene expression signature value)]. A higher survival predictor score was associated with worse outcome. According to a likelihood ratio test adjusted for the number of variables included, this model was significant in predicting survival in both the training set (p=1.8×10⁻⁸) and the validation set (p=2.0×10⁻⁵). For the 187 FL samples with available clinical data, the survival predictor score had a mean of −11.9 and a standard deviation of 0.9418, with each unit increase in the predictor score corresponding to a 2.5 fold increase in the relative risk of death. Data for all 191 samples is shown in Table 2365.

TABLE 2365 B cell T-cell Macro- differentiation sig- phage Survival Sample signature nature signature predictor ID # Set value value value score FL_1073 Training 9.70 9.14 8.58 −10.89 FL_1074 Training 11.11 9.06 8.52 −11.84 FL_1075 Validation 11.23 8.92 8.75 −11.15 FL_1076 Training 10.02 9.21 8.59 −11.25 FL_1077 Training 9.94 9.77 8.44 −12.82 FL_1078 Training 10.67 9.32 8.21 −12.76 FL_1080 Training 10.62 9.44 8.88 −11.64 FL_1081 Validation 10.38 9.00 8.09 −12.04 FL_1083 Training 10.29 9.77 8.74 −12.47 FL_1085 Validation 9.87 9.24 8.43 −11.55 FL_1086 Validation 10.03 9.50 9.02 −11.06 FL_1087 Training 9.83 9.98 9.37 −11.31 FL_1088 Validation 10.57 9.21 8.29 −12.27 FL_1089 Training 10.30 9.38 8.27 −12.53 FL_1090 Validation 9.74 9.24 8.20 −11.93 FL_1097 Validation 9.57 9.82 8.80 −11.93 FL_1098 Validation 11.08 9.40 8.97 −11.69 FL_1099 Training 10.23 9.70 9.12 −11.46 FL_1102 Validation 9.66 9.46 8.90 −10.93 FL_1104 Training 10.72 9.19 8.20 −12.53 FL_1106 Validation 11.11 9.17 9.57 −9.96 FL_1107 Training 9.70 9.42 9.55 −9.54 FL_1183 Training 9.85 9.25 8.44 −11.54 FL_1184 Training 10.12 9.57 8.86 −11.63 FL_1185 Validation 10.75 9.21 9.13 −10.68 FL_1186 Training 9.76 8.88 8.83 −9.80 FL_1416 Validation 9.94 9.45 8.59 −11.77 FL_1417 Training 10.12 8.53 8.43 −10.08 FL_1418 Validation 9.35 8.86 8.27 −10.59 FL_1419 Training 10.20 9.76 8.53 −12.81 FL_1422 Training 10.22 9.48 8.40 −12.43 FL_1425 Validation 9.61 8.89 8.58 −10.23 FL_1426 Training 10.80 9.06 8.13 −12.41 FL_1427 Training 10.27 8.56 8.13 −10.87 FL_1428 Validation 10.76 9.25 8.38 −12.32 FL_1432 Training 10.51 9.17 9.04 −10.59 FL_1436 Training 9.69 9.40 8.61 −11.42 FL_1440 Training 9.82 9.04 8.21 −11.50 FL_1445 Training 9.24 8.69 8.62 −9.41 FL_1450 Validation 9.70 9.88 10.37 −8.93 FL_1472 Validation 10.78 8.96 8.51 −11.40 FL_1473 Training 9.99 9.70 8.41 −12.75 FL_1474 Validation 10.21 9.27 9.05 −10.59 FL_1476 Validation 9.82 9.44 8.78 −11.27 FL_1477 Training 9.32 9.61 9.03 −10.78 FL_1478 Training 10.19 9.60 8.81 −11.83 FL_1479 Training 10.69 8.78 9.09 −9.71 FL_1480 Training 10.10 9.42 8.70 −11.57 FL_1579 Training 10.15 8.82 8.24 −11.15 FL_1580 Training 10.31 9.59 8.50 −12.54 FL_1581 Training 9.91 8.96 9.05 −9.66 FL_1582 Validation 9.73 8.31 8.06 −10.03 FL_1583 Training 10.95 9.45 8.86 −11.95 FL_1584 Training 9.98 9.38 8.46 −11.89 FL_1585 Validation 10.53 8.88 8.46 −11.11 FL_1586 Validation 10.00 9.30 8.42 −11.81 FL_1588 Training 9.59 9.41 8.94 −10.68 FL_1589 Training 10.29 9.68 8.73 −12.27 FL_1591 Training 10.44 9.45 8.56 −12.18 FL_1594 Validation 10.01 9.25 8.56 −11.41 FL_1595 Training 9.61 9.75 9.65 −10.07 FL_1598 Validation 11.18 8.80 8.31 −11.71 FL_1599 Validation 10.55 9.48 8.60 −12.24 FL_1603 Training 9.40 9.60 9.77 −9.31 FL_1604 Training 9.92 9.21 8.90 −10.54 FL_1606 Validation 9.87 9.45 9.17 −10.52 FL_1607 Validation 9.76 9.37 8.50 −11.63 FL_1608 Validation 9.92 8.90 8.39 −10.85 FL_1610 Validation 10.02 9.38 9.74 −9.30 FL_1611 Validation 10.18 9.41 8.69 −11.64 FL_1616 Training 9.62 9.33 8.85 −10.71 FL_1617 Validation 9.90 8.95 8.39 −10.98 FL_1619 Validation 9.98 9.37 8.47 −11.85 FL_1620 Validation 9.43 8.95 8.12 −11.19 FL_1622 Training 9.84 9.15 8.31 −11.56 FL_1623 Training 9.95 9.61 8.97 −11.37 FL_1624 Validation 10.55 9.06 8.43 −11.61 FL_1625 Validation 10.00 8.89 8.23 −11.22 FL_1626 Validation 11.05 8.62 8.10 −11.62 FL_1628 Validation 10.08 9.81 8.66 −12.57 FL_1637 Validation 9.77 9.95 9.59 −10.76 FL_1638 Validation 10.25 9.20 9.07 −10.41 FL_1639 Training 10.29 9.52 8.99 −11.35 FL_1643 Training 9.80 9.72 9.00 −11.46 FL_1644 Validation 9.51 9.46 8.61 −11.43 FL_1645 Training 9.39 9.46 8.70 −11.15 FL_1646 Training 9.90 9.25 8.52 −11.42 FL_1647 Training 9.51 9.12 8.95 −9.92 FL_1648 Training 10.02 9.18 7.86 −12.67 FL_1652 Training 9.62 9.39 9.19 −10.16 FL_1654 Validation 10.32 8.59 8.10 −11.02 FL_1655 Training 10.12 9.53 8.75 −11.74 FL_1656 Validation 10.54 9.08 8.55 −11.42 FL_1657 Training 10.53 9.53 8.55 −12.46 FL_1660 Training 10.24 8.75 8.27 −10.99 FL_1661 Validation 10.08 9.85 9.00 −11.97 FL_1662 Validation 9.85 9.56 9.49 −10.11 FL_1664 Validation 10.16 9.35 8.48 −11.92 FL_1669 Training 9.48 8.76 8.28 −10.45 FL_1670 Training 9.76 9.66 9.66 −9.92 FL_1675 Training 10.57 9.28 8.41 −12.18 FL_1681 Validation 10.48 9.52 8.66 −12.19 FL_1683 Validation 9.88 9.92 9.07 −11.83 FL_1684 Training 9.64 9.53 8.85 −11.20 FL_1716 Validation 9.90 8.91 8.22 −11.23 FL_1717 Validation 9.87 9.34 8.95 −10.71 FL_1718 Training 10.00 9.21 7.98 −12.49 FL_1719 Validation 9.87 9.06 8.42 −11.14 FL_1720 Training 10.70 8.77 8.92 −10.05 FL_1729 Training 10.50 9.23 8.65 −11.53 FL_1732 Validation 9.91 7.68 8.54 −7.69 FL_1761 Validation 9.81 9.22 8.39 −11.54 FL_1764 Training 9.81 9.24 8.77 −10.80 FL_1768 Training 10.12 9.36 8.50 −11.86 FL_1771 Training 9.92 9.12 8.68 −10.79 FL_1772 Validation 9.72 9.42 8.43 −11.87 FL_1788 Training 9.65 9.05 9.12 −9.51 FL_1790 Training 9.58 9.83 9.48 −10.56 FL_1792 Validation 9.79 9.29 8.67 −11.11 FL_1795 Training 9.58 10.18 9.33 −11.69 FL_1797 Validation 9.93 9.26 8.79 −10.90 FL_1799 Training 10.49 9.28 8.64 −11.65 FL_1810 Validation 10.06 8.55 8.21 −10.52 FL_1811 Validation 9.84 9.37 8.08 −12.56 FL_1825 Training 10.49 9.44 9.03 −11.24 FL_1827 Training 10.06 9.76 8.84 −12.08 FL_1828 Validation 10.55 8.93 7.67 −12.87 FL_1829 Validation 9.85 9.58 8.65 −11.87 FL_1830 Validation 10.80 8.99 8.67 −11.15 FL_1833 Training 10.41 9.83 8.82 −12.52 FL_1834 Validation 10.81 9.25 8.63 −11.85 FL_1835 Validation 9.36 9.25 8.91 −10.21 FL_1836 Validation 10.58 9.58 8.61 −12.50 FL_1837 Validation 10.22 9.47 8.76 −11.68 FL_1838 Validation 10.51 9.89 9.19 −11.98 FL_1839 Training 10.79 8.54 8.19 −11.09 FL_1841 Training 10.32 9.31 9.18 −10.48 FL_1842 Validation 10.36 9.69 8.92 −11.95 FL_1844 Validation 10.92 9.43 8.49 −12.65 FL_1845 Training 9.87 9.87 9.06 −11.73 FL_1846 Validation 9.66 9.81 9.93 −9.63 FL_1848 Training 9.82 9.74 8.70 −12.14 FL_1851 Training 9.89 9.47 9.03 −10.87 FL_1853 Validation 9.96 9.28 8.54 −11.49 FL_1854 Validation 9.97 9.29 8.73 −11.12 FL_1855 Validation 9.95 9.33 8.42 −11.85 FL_1857 Validation 10.35 9.81 9.28 −11.50 FL_1861 Validation 9.73 9.46 8.43 −11.96 FL_1862 Validation 10.42 8.94 8.22 −11.69 FL_1863 Validation 10.79 9.29 8.82 −11.54 FL_1864 Training 9.67 9.97 9.07 −11.80 FL_1866 Training 10.19 9.88 8.89 −12.33 FL_1870 Validation 9.78 10.07 9.30 −11.63 FL_1873 Training 10.09 9.41 8.77 −11.40 FL_1874 Validation 10.05 9.33 8.69 −11.37 FL_1876 Validation 10.15 9.59 8.67 −12.08 FL_1879 Training 9.73 9.21 8.58 −11.06 FL_1880 Validation 10.02 8.79 8.35 −10.77 FL_1882 Training 9.59 9.44 8.80 −11.05 FL_1884 Validation 9.76 9.51 9.26 −10.38 FL_1885 Validation 10.48 9.66 8.75 −12.32 FL_1887 Training 9.98 9.42 8.47 −11.96 FL_1888 Training 9.73 9.83 8.99 −11.67 FL_1890 Training 10.06 9.33 8.98 −10.76 FL_1894 Training 9.85 8.99 8.75 −10.29 FL_1896 Training 10.21 9.80 8.51 −12.94 FL_1897 Training 10.67 8.99 8.26 −11.90 FL_1898 Training 9.59 8.77 8.21 −10.68 FL_1900 Validation 10.12 9.10 8.10 −12.08 FL_1903 Validation 11.08 8.99 8.39 −11.93 FL_1904 Validation 10.20 9.16 8.30 −11.87 FL_1905 Validation 9.73 9.21 8.22 −11.80 FL_1906 Training 9.95 8.15 8.44 −9.01 FL_1907 Validation 10.12 7.95 7.99 −9.62 FL_1910 Training 11.03 9.38 8.74 −12.10 FL_1912 Validation 9.83 9.38 9.36 −9.95 FL_1913 Training 9.81 9.75 8.43 −12.69 FL_1916 Validation 9.83 9.18 8.40 −11.43 FL_1918 Validation 9.86 9.52 8.79 −11.45 FL_1919 Training 9.87 9.53 8.79 −11.48 FL_735 Validation 10.48 8.73 8.23 −11.20 FL_738 Validation 11.05 9.10 8.75 −11.43 FL_739 Training 9.66 9.25 8.74 −10.78 FL_878 Validation 10.61 8.92 8.65 −10.89 FL_879 Training 9.92 8.94 8.78 −10.14 FL_886 Validation 10.16 9.41 8.63 −11.73 FL_888 Validation 9.35 8.76 8.38 −10.15 FL_1627 Training 9.82 9.48 8.49 −11.94 FL_1429 Training 10.06 8.70 8.14 −11.01 FL_1850 Validation 9.58 9.73 8.70 −11.93 FL_1735 Validation 9.60 7.46 8.42 −7.19

In order to visualize the predictive power of the model, the FL samples were ranked according to their survival predictor scores and divided into four quartiles. Kaplan-Meier plots of overall survival showed clear differences in survival rate in the validation set (FIG. 8). The median survival for each of the four quartiles is set forth in Table 2366.

TABLE 2366 Median 5-year 10-year Quartile survival (yrs.) survival survival 1 NR 94% 79% 2 11.6  82% 62% 3 8.8 69% 39% 4 3.9 38% 22%

Example 5 Development of a Third FL Survival Predictor Using Gene Expression Data from the Lymph Dx Microarray

191 FL samples were divided into two equivalent groups: a training set for developing the survival prediction model, and a validation set for evaluating the reproducibility of the model. Gene expression data from the Lymph Dx microarray was obtained for those genes listed in Table 2364, above. This gene expression data was used to calculate gene expression signature values for the macrophage, T-cell, and B-cell differentiation gene expression signatures, and these signature values were used to generate a survival predictor score using the following equation: Survival predictor score=[1.51*(macrophage gene expression signature value)]−[2.11*(T-cell gene expression signature value)]−[0.505*(B-cell differentiation gene expression signature value)]. A higher survival predictor score was associated with worse outcome. For the 187 FL samples with available clinical data, the survival predictor score had a mean of −10.1 and a standard deviation of 0.69, with each unit increase in the predictor score corresponding to a 2.7 fold increase in the relative risk of death. Data for all 191 samples is shown in Table 2367.

TABLE 2367 B cell T-cell Macro- differentiation sig- phage Survival Sample signature nature signature predictor ID # Set value value value score FL_1073 Training 8.26 8.17 7.36 −10.30 FL_1074 Training 9.53 8.12 7.56 −10.53 FL_1075 Validation 9.81 8.00 7.99 −9.77 FL_1076 Training 8.46 8.10 7.62 −9.86 FL_1077 Training 8.45 8.66 7.32 −11.49 FL_1078 Training 9.23 8.32 7.32 −11.18 FL_1080 Training 9.18 8.37 7.86 −10.42 FL_1081 Validation 8.96 8.01 6.94 −10.96 FL_1083 Training 8.72 8.65 7.89 −10.75 FL_1085 Validation 8.34 8.17 7.54 −10.07 FL_1086 Validation 8.50 8.35 7.94 −9.94 FL_1087 Training 8.02 8.88 8.48 −10.00 FL_1088 Validation 9.10 8.15 7.38 −10.65 FL_1089 Training 8.76 8.31 7.35 −10.86 FL_1090 Validation 8.18 8.23 7.43 −10.28 FL_1097 Validation 8.07 8.81 7.90 −10.73 FL_1098 Validation 9.53 8.30 8.09 −10.11 FL_1099 Training 8.44 8.56 8.26 −9.86 FL_1102 Validation 7.92 8.43 7.94 −9.80 FL_1104 Training 9.17 8.07 7.21 −10.78 FL_1106 Validation 9.71 8.15 8.77 −8.85 FL_1107 Training 8.16 8.44 8.60 −8.95 FL_1183 Training 8.49 8.15 7.23 −10.56 FL_1184 Training 8.81 8.49 7.91 −10.43 FL_1185 Validation 9.31 8.19 8.06 −9.80 FL_1186 Training 8.43 7.87 7.83 −9.04 FL_1416 Validation 8.42 8.34 7.63 −10.34 FL_1417 Training 8.65 7.51 7.05 −9.58 FL_1418 Validation 7.96 7.82 7.22 −9.62 FL_1419 Training 8.80 8.71 7.55 −11.43 FL_1422 Training 8.63 8.35 7.39 −10.83 FL_1425 Validation 8.21 7.92 7.62 −9.36 FL_1426 Training 9.39 8.09 7.15 −11.01 FL_1427 Training 8.66 7.51 7.00 −9.65 FL_1428 Validation 9.33 8.18 7.39 −10.81 FL_1432 Training 8.98 8.17 7.93 −9.81 FL_1436 Training 8.04 8.17 7.35 −10.20 FL_1440 Training 8.29 7.82 7.15 −9.89 FL_1445 Training 8.04 7.78 7.63 −8.94 FL_1450 Validation 8.25 8.81 9.52 −8.39 FL_1472 Validation 9.29 7.88 7.33 −10.26 FL_1473 Training 8.49 8.57 7.52 −11.03 FL_1474 Validation 8.59 8.09 8.53 −8.54 FL_1476 Validation 8.25 8.39 7.71 −10.23 FL_1477 Training 7.94 8.57 7.88 −10.21 FL_1478 Training 8.57 8.40 7.88 −10.16 FL_1479 Training 9.15 7.83 7.87 −9.27 FL_1480 Training 8.25 8.38 7.44 −10.63 FL_1579 Training 8.70 7.73 7.43 −9.48 FL_1580 Training 8.86 8.46 7.64 −10.79 FL_1581 Training 8.41 7.89 8.08 −8.69 FL_1582 Validation 8.20 7.42 6.99 −9.24 FL_1583 Training 9.34 8.34 7.94 −10.32 FL_1584 Training 8.50 8.33 7.75 −10.17 FL_1585 Validation 9.08 7.96 7.72 −9.72 FL_1586 Validation 8.52 8.25 7.36 −10.61 FL_1588 Training 7.97 8.35 7.73 −9.98 FL_1589 Training 8.85 8.48 7.76 −10.66 FL_1591 Training 8.92 8.36 7.77 −10.42 FL_1594 Validation 8.54 8.22 7.74 −9.96 FL_1595 Training 8.05 8.82 8.68 −9.57 FL_1598 Validation 9.74 7.81 6.97 −10.88 FL_1599 Validation 9.13 8.42 7.69 −10.77 FL_1603 Training 7.97 8.66 8.90 −8.86 FL_1604 Training 8.47 8.14 7.75 −9.75 FL_1606 Validation 8.34 8.32 8.11 −9.51 FL_1607 Validation 8.33 8.30 7.39 −10.57 FL_1608 Validation 8.35 7.88 6.98 −10.31 FL_1610 Validation 8.48 8.35 8.86 −8.52 FL_1611 Validation 8.54 8.33 7.64 −10.37 FL_1616 Training 8.03 8.39 7.67 −10.18 FL_1617 Validation 8.30 7.85 7.52 −9.40 FL_1619 Validation 8.53 8.31 7.64 −10.32 FL_1620 Validation 8.09 7.99 7.17 −10.11 FL_1622 Training 8.14 8.10 7.36 −10.09 FL_1623 Training 8.45 8.52 8.15 −9.93 FL_1624 Validation 9.13 8.12 7.46 −10.49 FL_1625 Validation 8.53 7.94 7.17 −10.23 FL_1626 Validation 9.63 7.67 7.17 −10.22 FL_1628 Validation 8.63 8.76 7.95 −10.86 FL_1637 Validation 8.07 8.81 8.79 −9.38 FL_1638 Validation 8.52 8.18 8.19 −9.18 FL_1639 Training 8.70 8.33 7.89 −10.06 FL_1643 Training 8.26 8.62 8.01 −10.26 FL_1644 Validation 8.28 8.33 7.77 −10.02 FL_1645 Training 7.84 8.32 7.68 −9.91 FL_1646 Training 8.40 8.26 7.71 −10.01 FL_1647 Training 8.10 8.04 7.92 −9.10 FL_1648 Training 8.33 8.08 6.87 −10.90 FL_1652 Training 8.15 8.33 8.37 −9.07 FL_1654 Validation 8.67 7.62 7.03 −9.85 FL_1655 Training 8.53 8.41 7.75 −10.36 FL_1656 Validation 9.09 8.09 7.62 −10.16 FL_1657 Training 8.95 8.44 7.58 −10.89 FL_1660 Training 8.82 7.79 7.26 −9.93 FL_1661 Validation 8.56 8.79 8.17 −10.53 FL_1662 Validation 8.30 8.47 8.69 −8.93 FL_1664 Validation 8.62 8.23 7.56 −10.31 FL_1669 Training 7.89 7.67 7.39 −9.02 FL_1670 Training 8.01 8.54 8.64 −9.03 FL_1675 Training 9.00 8.21 7.36 −10.76 FL_1681 Validation 8.83 8.39 7.59 −10.72 FL_1683 Validation 8.14 8.85 7.97 −10.74 FL_1684 Training 7.99 8.42 7.84 −9.97 FL_1716 Validation 8.28 7.90 7.26 −9.88 FL_1717 Validation 8.27 8.21 7.89 −9.60 FL_1718 Training 8.50 8.17 7.15 −10.75 FL_1719 Validation 8.35 8.02 7.21 −10.26 FL_1720 Training 9.03 7.65 8.01 −8.61 FL_1729 Training 8.97 8.27 7.69 −10.37 FL_1732 Validation 8.49 6.82 7.71 −7.02 FL_1761 Validation 8.36 8.19 7.29 −10.49 FL_1764 Training 8.52 8.24 7.94 −9.69 FL_1768 Training 8.70 8.25 7.63 −10.28 FL_1771 Training 8.55 8.19 7.65 −10.04 FL_1772 Validation 8.30 8.38 7.41 −10.71 FL_1788 Training 8.14 8.06 8.11 −8.87 FL_1790 Training 7.95 8.69 8.36 −9.74 FL_1792 Validation 8.16 8.20 7.64 −9.88 FL_1795 Training 7.94 9.08 8.37 −10.54 FL_1797 Validation 8.17 8.21 7.87 −9.57 FL_1799 Training 9.02 8.21 7.77 −10.14 FL_1810 Validation 8.43 7.52 7.06 −9.47 FL_1811 Validation 8.33 8.24 7.07 −10.93 FL_1825 Training 8.90 8.39 7.97 −10.18 FL_1827 Training 8.47 8.77 7.96 −10.76 FL_1828 Validation 9.13 7.87 6.76 −11.01 FL_1829 Validation 8.34 8.51 7.59 −10.71 FL_1830 Validation 9.26 8.04 7.62 −10.13 FL_1833 Training 8.82 8.86 7.88 −11.26 FL_1834 Validation 9.25 8.17 7.62 −10.39 FL_1835 Validation 7.71 8.16 8.01 −9.02 FL_1836 Validation 9.06 8.52 7.59 −11.09 FL_1837 Validation 8.57 8.33 7.37 −10.79 FL_1838 Validation 8.78 8.72 8.04 −10.69 FL_1839 Training 9.27 7.36 7.37 −9.08 FL_1841 Training 8.66 8.35 8.17 −9.64 FL_1842 Validation 8.62 8.50 8.02 −10.19 FL_1844 Validation 9.37 8.40 7.47 −11.18 FL_1845 Training 8.33 8.84 8.30 −10.32 FL_1846 Validation 8.11 8.75 9.06 −8.89 FL_1848 Training 8.19 8.60 7.91 −10.33 FL_1851 Training 8.37 8.50 8.15 −9.84 FL_1853 Validation 8.37 8.14 7.43 −10.19 FL_1854 Validation 8.50 8.29 7.96 −9.78 FL_1855 Validation 8.63 8.34 7.54 −10.58 FL_1857 Validation 8.73 8.82 8.45 −10.26 FL_1861 Validation 8.21 8.50 7.50 −10.77 FL_1862 Validation 8.98 7.96 7.31 −10.28 FL_1863 Validation 9.30 8.22 7.86 −10.18 FL_1864 Training 8.13 8.93 8.27 −10.46 FL_1866 Training 8.62 8.78 7.91 −10.93 FL_1870 Validation 8.16 8.97 8.52 −10.18 FL_1873 Training 8.55 8.30 8.00 −9.74 FL_1874 Validation 8.43 8.20 7.59 −10.10 FL_1876 Validation 8.48 8.52 7.70 −10.64 FL_1879 Training 8.29 8.21 7.66 −9.94 FL_1880 Validation 8.56 7.76 7.34 −9.61 FL_1882 Training 8.02 8.40 7.71 −10.14 FL_1884 Validation 8.14 8.46 8.42 −9.24 FL_1885 Validation 8.88 8.57 7.78 −10.81 FL_1887 Training 8.38 8.39 7.38 −10.78 FL_1888 Training 8.14 8.74 8.07 −10.37 FL_1890 Training 8.45 8.24 8.11 −9.41 FL_1894 Training 8.38 7.97 7.82 −9.25 FL_1896 Training 8.63 8.71 7.52 −11.37 FL_1897 Training 9.01 7.91 6.93 −10.78 FL_1898 Training 8.08 7.75 7.09 −9.74 FL_1900 Validation 8.61 7.94 6.84 −10.77 FL_1903 Validation 9.63 7.96 7.30 −10.64 FL_1904 Validation 8.79 8.14 7.15 −10.82 FL_1905 Validation 8.22 8.24 7.36 −10.43 FL_1906 Training 8.40 7.40 7.24 −8.93 FL_1907 Validation 8.61 7.11 6.59 −9.40 FL_1910 Training 9.47 8.28 7.63 −10.73 FL_1912 Validation 8.32 8.45 8.52 −9.18 FL_1913 Training 8.24 8.60 7.23 −11.41 FL_1916 Validation 8.31 8.04 7.27 −10.19 FL_1918 Validation 8.30 8.49 7.78 −10.37 FL_1919 Training 8.05 8.42 8.00 −9.75 FL_735 Validation 9.03 7.83 7.41 −9.88 FL_738 Validation 9.54 8.07 7.65 −10.30 FL_739 Training 8.14 8.09 7.69 −9.57 FL_878 Validation 9.17 7.91 7.70 −9.69 FL_879 Training 8.37 7.96 7.67 −9.45 FL_886 Validation 8.59 8.38 7.67 −10.44 FL_888 Validation 7.85 7.71 7.07 −9.56 FL_1627 Training 8.26 8.17 7.36 −10.30 FL_1429 Training 9.53 8.12 7.56 −10.53 FL_1850 Validation 9.81 8.00 7.99 −9.77 FL_1735 Validation 8.46 8.10 7.62 −9.86

In order to visualize the predictive power of the model, the FL samples were ranked according to their survival predictor scores and divided into four quartiles. Kaplan-Meier plots of overall survival showed clear differences in survival rate in the validation set (FIG. 9).

Example 6 Development of a First DLBCL Survival Predictor Using Gene Expression Data from Affymetrix U133A and U133B Microarrays

Gene expression data from Affymetrix U133A and U133B microarrays was obtained for 231 DLBCL samples. The follow-up time and status at follow-up for each of the subjects from whom these samples were acquired is listed in Table 2368. Table 2368 also indicates which samples were used in creating the survival predictor.

TABLE 2368 Used in Length of follow- Status at creating survival Sample ID # up (years) follow-up predictor? ABC_1000 0.69 Dead Yes ABC_1002 0.28 Dead Yes ABC_1023 5.57 Dead Yes ABC_1027 0.25 Dead Yes ABC_1031 6.64 Dead Yes ABC_1034 2.31 Dead Yes ABC_1038 0.71 Dead Yes ABC_1043 2.31 Dead Yes ABC_1045 2.26 Dead Yes ABC_1055 7.81 Alive Yes ABC_1057 2.13 Dead Yes ABC_1059 2.00 Dead Yes ABC_1061 1.04 Dead Yes ABC_1946 0.68 Dead No ABC_1994 1.21 Dead No ABC_2001 1.32 Dead No ABC_304 1.31 Dead Yes ABC_305 0.82 Alive Yes ABC_309 2.80 Alive Yes ABC_413 0.60 Dead Yes ABC_428 11.38 Alive Yes ABC_432 0.38 Dead Yes ABC_446 2.82 Dead Yes ABC_462 7.49 Dead Yes ABC_477 1.70 Dead Yes ABC_481 10.75 Alive Yes ABC_482 7.72 Alive Yes ABC_538 0.34 Dead Yes ABC_541 4.11 Alive Yes ABC_544 1.31 Dead Yes ABC_547 0.05 Dead Yes ABC_577 1.65 Alive Yes ABC_616 0.99 Dead Yes ABC_626 2.49 Dead Yes ABC_633 2.02 Alive Yes ABC_642 0.34 Dead Yes ABC_644 0.31 Dead Yes ABC_645 6.08 Dead Yes ABC_646 2.59 Dead Yes ABC_651 2.34 Alive Yes ABC_652 0.01 Dead Yes ABC_660 0.20 Dead Yes ABC_663 0.62 Dead Yes ABC_668 6.44 Alive Yes ABC_676 1.00 Dead Yes ABC_678 0.06 Dead Yes ABC_687 0.94 Dead Yes ABC_689 2.54 Dead Yes ABC_692 10.53 Alive Yes ABC_694 4.83 Alive Yes ABC_700 5.40 Dead Yes ABC_702 4.13 Dead Yes ABC_704 9.67 Alive Yes ABC_709 0.47 Dead Yes ABC_712 3.26 Dead Yes ABC_714 2.45 Dead Yes ABC_717 0.42 Dead Yes ABC_725 0.96 Dead Yes ABC_726 7.62 Alive Yes ABC_730 1.03 Dead Yes ABC_753 0.04 Dead Yes ABC_756 7.21 Alive Yes ABC_771 6.80 Dead Yes ABC_779 0.35 Dead Yes ABC_800 0.33 Dead Yes ABC_807 0.31 Dead Yes ABC_809 0.51 Dead Yes ABC_816 1.86 Dead Yes ABC_820 1.59 Dead Yes ABC_823 0.16 Dead Yes ABC_835 1.22 Dead Yes ABC_839 0.29 Dead Yes ABC_841 10.14 Alive Yes ABC_858 3.58 Dead Yes ABC_872 5.00 Alive Yes ABC_875 8.45 Alive Yes ABC_912 16.79 Alive Yes ABC_996 0.21 Dead Yes GCB_1005 5.77 Alive Yes GCB_1008 6.46 Alive Yes GCB_1009 9.68 Alive Yes GCB_1021 14.59 Alive Yes GCB_1025 2.86 Dead Yes GCB_1026 6.94 Dead Yes GCB_1037 0.23 Dead Yes GCB_1039 2.05 Dead Yes GCB_1049 1.33 Dead Yes GCB_1051 0.12 Dead Yes GCB_1058 0.42 Dead Yes GCB_1060 6.45 Alive Yes GCB_1990 0.06 Dead No GCB_1991 1.01 Dead No GCB_2017 0.08 Dead No GCB_2018 0.17 Dead No GCB_2095 0.97 Alive No GCB_412 12.12 Alive Yes GCB_415 5.38 Dead Yes GCB_421 1.24 Dead Yes GCB_424 10.62 Dead Yes GCB_433 0.76 Dead Yes GCB_434 10.53 Alive Yes GCB_438 8.15 Alive Yes GCB_459 9.65 Alive Yes GCB_470 11.17 Alive Yes GCB_479 7.24 Alive Yes GCB_492 11.29 Alive Yes GCB_517 3.03 Dead Yes GCB_523 8.36 Alive Yes GCB_524 5.88 Alive Yes GCB_529 1.06 Dead Yes GCB_533 0.71 Dead Yes GCB_537 4.99 Dead Yes GCB_543 3.47 Alive Yes GCB_545 1.10 Dead Yes GCB_549 2.68 Dead Yes GCB_550 21.78 Alive Yes GCB_553 0.82 Dead Yes GCB_565 9.11 Dead Yes GCB_572 14.24 Alive Yes GCB_617 5.88 Alive Yes GCB_618 5.65 Alive Yes GCB_619 8.76 Alive Yes GCB_623 2.43 Alive Yes GCB_627 1.27 Dead Yes GCB_654 7.37 Alive Yes GCB_661 0.56 Alive Yes GCB_669 7.11 Alive Yes GCB_672 6.78 Alive Yes GCB_674 7.22 Alive Yes GCB_675 6.02 Alive Yes GCB_681 9.70 Alive Yes GCB_688 0.33 Dead Yes GCB_695 0.15 Dead Yes GCB_698 3.88 Alive Yes GCB_701 3.90 Alive Yes GCB_710 1.08 Dead Yes GCB_711 3.93 Dead Yes GCB_722 3.32 Alive Yes GCB_724 1.40 Dead Yes GCB_731 10.18 Alive Yes GCB_742 4.09 Alive Yes GCB_744 8.86 Alive Yes GCB_745 1.33 Dead Yes GCB_747 15.41 Alive Yes GCB_749 10.40 Alive Yes GCB_758 1.10 Dead Yes GCB_772 2.48 Alive Yes GCB_777 4.27 Dead Yes GCB_792 5.53 Alive Yes GCB_795 3.43 Alive Yes GCB_797 6.87 Dead Yes GCB_803 1.45 Dead Yes GCB_810 11.72 Alive Yes GCB_817 2.76 Dead Yes GCB_818 0.10 Dead Yes GCB_819 0.72 Dead Yes GCB_821 9.47 Alive Yes GCB_832 4.01 Alive Yes GCB_836 4.29 Alive Yes GCB_840 3.40 Alive Yes GCB_847 4.16 Alive Yes GCB_860 3.03 Dead Yes GCB_871 0.41 Dead Yes GCB_874 0.12 Dead Yes GCB_995 6.65 Alive Yes PMBL_1006 7.12 Alive Yes PMBL_1024 19.83 Alive Yes PMBL_1048 7.70 Alive Yes PMBL_1053 1.04 Dead Yes PMBL_1920 1.97 Alive No PMBL_1921 4.16 Alive No PMBL_1923 1.60 Alive No PMBL_1924 6.11 Alive No PMBL_1935 12.42 Alive No PMBL_1941 0.71 Alive No PMBL_1942 0.88 Alive No PMBL_1943 8.96 Alive No PMBL_1945 0.84 Dead No PMBL_1948 7.96 Alive No PMBL_1949 4.28 Alive No PMBL_1989 1.33 Dead No PMBL_1992 1.00 Dead No PMBL_1993 1.33 Dead No PMBL_2002 6.62 Alive No PMBL_2019 0.99 Dead No PMBL_2020 2.08 Alive No PMBL_2092 1.27 Alive No PMBL_484 1.40 Dead Yes PMBL_546 0.78 Dead Yes PMBL_570 14.40 Alive Yes PMBL_621 8.14 Alive Yes PMBL_638 0.70 Dead Yes PMBL_691 0.32 Dead Yes PMBL_791 1.33 Dead Yes PMBL_824 12.24 Alive Yes PMBL_906 16.80 Alive Yes PMBL_994 4.79 Alive Yes PMBL_998 9.11 Alive Yes UC_DLBCL_1001 0.33 Dead Yes UC_DLBCL_1004 6.72 Alive Yes UC_DLBCL_1007 2.26 Dead Yes UC_DLBCL_1018 0.03 Dead Yes UC_DLBCL_1041 3.13 Dead Yes UC_DLBCL_1054 12.34 Alive Yes UC_DLBCL_306 2.69 Alive Yes UC_DLBCL_310 0.97 Alive Yes UC_DLBCL_449 9.16 Alive Yes UC_DLBCL_452 9.17 Alive Yes UC_DLBCL_458 1.18 Dead Yes UC_DLBCL_460 9.02 Alive Yes UC_DLBCL_491 4.47 Dead Yes UC_DLBCL_528 1.64 Alive Yes UC_DLBCL_615 4.94 Alive Yes UC_DLBCL_625 5.24 Alive Yes UC_DLBCL_664 0.62 Dead Yes UC_DLBCL_671 3.35 Alive Yes UC_DLBCL_682 0.11 Dead Yes UC_DLBCL_683 7.42 Alive Yes UC_DLBCL_684 1.92 Dead Yes UC_DLBCL_748 1.01 Dead Yes UC_DLBCL_751 9.99 Alive Yes UC_DLBCL_808 0.37 Dead Yes UC_DLBCL_831 11.02 Dead Yes UC_DLBCL_834 1.64 Dead Yes UC_DLBCL_838 0.00 Dead Yes UC_DLBCL_851 0.05 Dead Yes UC_DLBCL_854 1.51 Dead Yes UC_DLBCL_855 1.67 Alive Yes UC_DLBCL_856 0.60 Dead Yes

The correlation between expression of each gene represented on the microarrays and survival was estimated using a Cox proportional hazards model. The results of this survival analysis are provided in the final two columns of Table 1723. The first of these two columns (“DLBCL_Cox_coefficient”) provides a Cox coefficient indicating the extent to which a 2-fold increase in expression of a particular gene affects mortality. A positive Cox coefficient indicates increasing mortality with increasing expression of the gene, while a negative Cox coefficient indicates decreasing mortality with increasing expression of the gene. The second of these two columns (“DLBCL_Cox_P_value”) provides a Cox p-value indicating the estimated probability that the increase or decrease in survival associated with the gene would occur by chance if there was no connection between the expression of the gene and survival.

Genes that were significantly correlated with survival (p<0.001) were grouped into gene expression signatures using a hierarchical clustering algorithm. The expression level of every component gene in each of these gene expression signatures was averaged for each sample to create a gene expression signature value. A step-up procedure (Drapner 1966) was applied to determine the optimal number of gene signatures to use in the survival predictor model. First, the gene expression signature that was most significantly associated with survival was included in the model. Next, the gene expression signature with the second highest association with survival was added to the model to form a two-component model. This procedure was repeated until there was no gene expression signature to add to the model with a p-value of <0.05.

The final prediction model incorporated gene expression signature values from three gene expression signatures. The first gene expression signature added to the model was termed “ABC DLBCL high,” because it included genes that were more highly expressed in ABC than in GCB (Rosenwald 2002). The second gene expression signature added to the model was termed “lymph node,” because it reflected the response of non-tumor cells in the lymph node to the malignant lymphoma cells. The final gene expression signature added to the model was termed “MHC class II,” because it included all of the genes encoding the MHC class II alpha and beta chains. Table 2369 shows the genes that were averaged to form each of these signatures.

TABLE 2369 Survival Signature UNIQID Gene symbol p-value ABC DLBCL high 1134271 POU5F1 3.09E−05 ABC DLBCL high 1121564 DRIL1 4.06E−05 ABC DLBCL high 1119889 PDCD4 7.28E−05 ABC DLBCL high 1133300 CTH 1.23E−04 ABC DLBCL high 1106030 MGC: 50789 1.70E−04 ABC DLBCL high 1139301 FLJ20150 4.49E−04 ABC DLBCL high 1122131 CHST7 5.18E−04 ABC DLBCL high 1114824 LIMD1 5.20E−04 ABC DLBCL high 1100161 LOC142678 6.24E−04 ABC DLBCL high 1120129 TLE1 6.95E−04 Lymph node 1097126 TEM8 5.14E−09 Lymph node 1120880 LTBP2 9.80E−07 Lymph node 1098898 FLJ31066 1.09E−06 Lymph node 1123376 RARRES2 1.68E−06 Lymph node 1128945 SLC12A8 2.90E−06 Lymph node 1130994 DPYSL3 3.37E−06 Lymph node 1124429 SULF1 3.53E−06 Lymph node 1099358 FLJ39971 4.09E−06 Lymph node 1130509 SPARC 6.23E−06 Lymph node 1095985 TMEPAI 7.07E−06 Lymph node 1123038 ACTN1 7.90E−06 Lymph node 1133700 CDH11 8.20E−06 Lymph node 1122101 TFEC 9.66E−06 Lymph node 1124296 SDC2 9.99E−06 MHC Class II 1123127 HLA-DRA 1.21E−06 MHC Class II 1136777 HLA-DQA1 3.45E−06 MHC Class II 1137771 HLA-DRB1 3.95E−06 MHC Class II 1134281 HLA-DRB4 2.70E−05 MHC Class II 1136573 HLA-DPA1 2.92E−05 MHC Class II 1132710 HLA-DRB3 7.09E−05

Fitting the Cox proportional hazards model to the three gene expression signature values resulted in the following model: Survival predictor score=[0.586*(ABC DLBCL high gene expression signature value)]−[0.468*(lymph node gene expression signature value)]−[0.336*(MHC Class II gene expression signature value)]. A higher survival predictor score was associated with worse outcome. According to a likelihood ratio test adjusted for the number of variables included, this model was significant in predicting survival at p=2.13×10⁻¹³. In order to visualize the predictive power of the model, the 205 samples used to create the model were ranked according to their survival predictor scores and divided into four quartiles. Kaplan-Meier plots of overall survival probability show clear differences in survival rate between these four quartiles (FIG. 10). The five-year survival probabilities for each quartile are set forth in Table 2370.

TABLE 2370 Quartile 5-year survival 1 83% 2 59% 3 33% 4 17%

Example 7 Development of a Second DLBCL Survival Predictor Using Gene Expression Data from the Lymph Dx Microarray

A DLBCL survival model based on gene expression had been developed previously using proliferation, germinal center B-cell, lymph node, and MHC class II gene expression signatures and the expression of the single gene BMP-6 (Rosenwald 2002). BMP-6 expression was poorly measured on the Lymph Dx microarray, but genes associated with each of these four gene expression signatures exhibited associations with survival similar to those observed using Lymphochip microarrays. DLBCL samples were divided into two groups: a training set (100 samples) for developing the survival prediction model, and a validation set (100 samples) for evaluating the reproducibility of the model. Gene expressed in the training set samples were clustered, and lymph node, germinal center B-cell, MHC class II, and proliferation gene expression signatures were identified. Within each signature, expression of genes that were associated with survival (p<0.01) was averaged to generate a gene expression signature value for each signature. Table 2371 lists the genes that were used to generate the gene expression signature value for each signature.

TABLE 2371 Signature UNIQID Unigene ID Build 167 Gene symbol Germinal center B-cell 1099686 117721 Germinal center B-cell 1099711 243596 Germinal center B-cell 1103390 271752 BPNT1 Germinal center B-cell 1106025 49500 KIAA0746 Germinal center B-cell 1128287 300063 ASB13 Germinal center B-cell 1132520 283063 LMO2 Germinal center B-cell 1138192 126608 NR3C1 Germinal center B-cell 1529318 291954 Germinal center B-cell 1529344 317970 SERPINA11 Germinal center B-cell 1529352 446195 Germinal center B-cell 1096570 409813 ANUBL1 Germinal center B-cell 1097897 266175 PAG Germinal center B-cell 1097901 266175 PAG Germinal center B-cell 1098611 433611 PDK1 Germinal center B-cell 1100581 155024 BCL6 Germinal center B-cell 1115034 387222 NEK6 Germinal center B-cell 1120090 155024 BCL6 Germinal center B-cell 1120946 25209 MAPK10 Germinal center B-cell 1121248 54089 BARD1 Germinal center B-cell 1123105 434281 PTK2 Germinal center B-cell 1125456 300592 MYBL1 Germinal center B-cell 1128694 171466 ELL3 Germinal center B-cell 1128787 114611 C7orf10 Germinal center B-cell 1132122 307734 MME Germinal center B-cell 1136269 101474 MAST2 Germinal center B-cell 1136702 155584 KIAA0121 Germinal center B-cell 1139230 29724 PLEKHF2 Germinal center B-cell 1529292 NA Germinal center B-cell 1529295 116441 Lymph node 1097126 274520 ANTXR1 Lymph node 1099028 334838 FNDC1 Lymph node 1099358 93135 Lymph node 1101478 146246 MGC45780 Lymph node 1103497 50115 Lymph node 1121029 412999 CSTA Lymph node 1124429 409602 SULF1 Lymph node 1135068 71719 PDLIM3 Lymph node 1136051 520937 CSF2RA Lymph node 1136172 38084 SULT1C1 MHC class II 1136777 387679 HLA-DQA1 MHC class II 1136877 409934 HLA-DQB1 Proliferation 1096903 437460 FLJ10385 Proliferation 1120583 153768 RNU3IP2 Proliferation 1123289 5409 POLR1C Proliferation 1131808 75447 RALBP1 Proliferation 1133102 360041 FRDA Proliferation 1136595 404814 VDAC1

Table 2372 lists p-values for the association of each signature with survival in the training set, the validation set, and overall.

TABLE 2372 Signature Training set Validation set Overall Lymph node 4.0 × 10⁻⁵ 2.3 × 10⁻⁶  6.8 × 10⁻¹⁰ Proliferation 8.1 × 10⁻⁵ 3.4 × 10⁻³ 2.1 × 10⁻⁶ Germinal center B-cell 6.2 × 10⁻⁶ 2.1 × 10⁻³ 5.0 × 10⁻⁸ MHC class II 2.4 × 10⁻² 2.7 × 10⁻³ 3.1 × 10⁻⁴

The four gene expression signatures were used to generate a survival predictor score using the following equation: Survival predictor score=[−0.4337*(lymph node gene expression signature value)]+[0.09*(proliferation gene expression signature value)]−[0.4144*(germinal center B-cell gene expression signature value)]−[0.2006*(MHC class II gene expression signature value)]. A higher survival predictor score was associated with worse outcome. For the 200 DLBCL samples used to generate the model, the survival predictor score had a mean of 5.7 and a standard deviation of 0.78, with each unit increase in the predictor score corresponding to an approximately 2.7 fold increase in the relative risk of death. Data for all 200 samples is presented in Table 2373.

TABLE 2373 Germinal Lymph center B- MHC node Proliferation cell class II Survival signature signature signature signature predictor Sample ID # Set value value value value score ABC_1000 Validation 6.50 8.92 7.60 11.50 −5.08 ABC_1002 Validation 7.00 8.58 7.27 12.54 −5.50 ABC_1023 Validation 7.43 8.99 6.80 11.42 −5.05 ABC_1027 Training 5.68 9.00 6.87 12.31 −4.70 ABC_1031 Validation 8.02 9.00 7.17 11.68 −5.53 ABC_1034 Validation 6.06 9.61 6.72 11.83 −4.58 ABC_1038 Training 6.83 8.97 7.17 12.30 −5.23 ABC_1043 Training 6.96 9.01 6.77 12.29 −5.11 ABC_1045 Validation 8.18 8.21 6.77 12.07 −5.66 ABC_1055 Validation 5.58 9.16 7.30 13.05 −4.76 ABC_1057 Training 7.33 8.94 7.74 12.05 −5.53 ABC_1059 Validation 9.02 8.46 7.15 11.35 −6.08 ABC_1061 Training 7.13 9.18 7.09 12.28 −5.21 ABC_304 Validation 5.92 8.80 6.76 12.76 −4.84 ABC_305 Training 5.92 8.74 7.50 11.89 −4.91 ABC_309 Validation 8.86 8.39 7.62 12.53 −6.46 ABC_413 Validation 6.45 9.32 6.55 9.04 −4.16 ABC_428 Training 7.52 9.19 7.98 10.25 −5.51 ABC_432 Validation 6.48 9.33 7.45 9.56 −4.56 ABC_446 Training 7.91 9.42 7.41 10.55 −5.46 ABC_462 Validation 6.41 8.85 6.67 13.36 −5.03 ABC_477 Validation 6.26 9.02 6.69 12.45 −4.89 ABC_481 Training 8.18 8.30 7.35 11.98 −5.91 ABC_482 Training 8.59 9.01 7.66 12.35 −6.16 ABC_538 Validation 8.06 8.84 7.17 11.83 −5.69 ABC_541 Training 6.14 8.52 7.42 10.59 −4.71 ABC_544 Training 6.91 9.03 6.82 11.87 −4.89 ABC_547 Validation 5.80 8.96 7.14 11.38 −4.60 ABC_577 Validation 7.84 8.65 8.16 11.95 −5.94 ABC_616 Validation 6.03 9.05 7.36 12.64 −4.84 ABC_626 Validation 7.48 9.22 7.25 11.11 −5.27 ABC_633 Training 7.74 8.35 7.39 12.45 −5.80 ABC_642 Training 5.71 8.82 6.41 13.80 −4.62 ABC_644 Validation 6.64 9.15 7.05 13.28 −5.20 ABC_645 Training 8.44 8.81 7.93 13.39 −6.43 ABC_646 Validation 5.94 9.11 6.71 11.60 −4.63 ABC_652 Validation 5.87 8.85 6.88 12.73 −4.77 ABC_660 Training 5.19 9.34 6.64 10.17 −3.86 ABC_663 Training 5.69 9.02 7.33 12.82 −4.91 ABC_668 Validation 7.12 9.28 7.03 10.57 −4.91 ABC_676 Training 4.95 8.90 7.09 13.32 −4.61 ABC_678 Training 5.84 9.11 7.34 11.26 −4.41 ABC_687 Validation 5.15 9.89 6.56 10.46 −3.76 ABC_689 Training 6.49 8.86 7.10 12.56 −4.88 ABC_692 Validation 7.32 8.96 7.25 11.57 −5.32 ABC_694 Validation 8.28 9.21 8.01 12.41 −6.23 ABC_700 Training 7.29 8.97 7.55 12.10 −5.48 ABC_702 Validation 7.60 8.66 6.86 12.55 −5.45 ABC_704 Training 7.07 8.92 7.03 12.83 −5.35 ABC_709 Validation 5.92 8.58 6.37 13.40 −4.66 ABC_712 Validation 5.79 9.12 6.34 12.02 −4.23 ABC_714 Training 7.49 8.88 7.49 11.97 −5.54 ABC_717 Training 7.17 9.45 7.01 11.34 −5.05 ABC_725 Training 6.71 9.01 6.52 12.76 −4.86 ABC_726 Validation 6.91 8.72 6.71 11.91 −4.90 ABC_730 Validation 6.28 9.22 7.28 12.14 −4.88 ABC_753 Training 6.84 9.64 7.05 13.00 −5.22 ABC_756 Training 7.67 8.45 7.59 12.48 −5.85 ABC_771 Training 6.98 8.76 6.91 12.20 −5.18 ABC_779 Training 6.73 9.32 6.78 9.82 −4.44 ABC_800 Validation 8.75 8.31 7.45 11.91 −6.04 ABC_807 Training 5.50 9.53 6.92 7.56 −3.79 ABC_809 Training 7.40 8.70 7.68 10.83 −5.50 ABC_816 Training 5.20 9.91 7.65 10.64 −4.14 ABC_820 Training 6.71 8.94 6.55 11.98 −4.85 ABC_823 Validation 5.58 9.26 6.44 10.09 −3.97 ABC_835 Validation 6.95 8.68 8.04 12.31 −5.59 ABC_839 Training 6.63 9.17 7.23 11.89 −5.04 ABC_841 Validation 6.35 9.51 7.52 13.19 −5.28 ABC_858 Training 7.63 8.51 7.12 11.74 −5.42 ABC_872 Training 6.78 8.73 7.41 12.47 −5.44 ABC_875 Training 7.59 8.81 7.20 11.26 −5.25 ABC_912 Validation 7.01 8.55 7.45 12.79 −5.64 ABC_996 Validation 5.00 9.53 6.70 10.02 −3.94 GCB_1005 Validation 8.28 8.67 9.11 13.27 −6.98 GCB_1008 Training 8.17 8.59 9.83 12.83 −7.06 GCB_1009 Training 6.63 9.02 10.07 12.28 −6.19 GCB_1021 Validation 6.44 8.83 9.34 13.20 −6.15 GCB_1025 Validation 7.87 8.48 9.27 12.37 −6.57 GCB_1026 Training 7.71 8.30 9.81 13.52 −6.85 GCB_1037 Training 4.95 8.83 9.35 12.57 −5.22 GCB_1039 Training 7.63 8.65 9.01 13.28 −6.47 GCB_1049 Validation 8.54 8.61 8.12 12.60 −6.41 GCB_1051 Validation 6.26 9.09 9.48 12.76 −5.97 GCB_1058 Validation 7.12 8.89 8.34 12.80 −5.85 GCB_1060 Validation 8.27 8.84 8.94 12.96 −6.75 GCB_412 Training 7.22 8.33 8.50 13.09 −6.09 GCB_415 Training 9.01 8.62 8.38 11.99 −6.47 GCB_421 Training 7.59 7.89 7.49 12.20 −5.80 GCB_424 Training 9.29 8.42 8.51 12.44 −6.79 GCB_433 Training 8.45 8.34 8.02 12.64 −6.54 GCB_434 Training 8.46 8.55 9.17 12.54 −6.98 GCB_438 Validation 8.14 8.71 9.13 12.51 −6.67 GCB_459 Validation 8.98 8.39 8.42 11.37 −6.49 GCB_470 Validation 7.72 8.57 8.67 12.23 −6.12 GCB_479 Validation 6.86 8.25 7.13 13.07 −5.35 GCB_492 Training 8.01 8.61 9.51 12.34 −6.63 GCB_517 Validation 8.57 8.73 7.99 12.76 −6.48 GCB_523 Training 5.96 8.56 8.74 12.77 −5.72 GCB_524 Training 8.51 8.09 8.76 12.51 −6.57 GCB_529 Training 5.12 9.17 8.88 10.77 −4.86 GCB_533 Training 8.88 8.81 8.36 12.44 −6.60 GCB_537 Validation 7.42 8.19 9.73 13.29 −6.68 GCB_543 Validation 8.49 8.02 8.66 12.06 −6.45 GCB_545 Training 8.65 8.28 6.90 12.90 −6.13 GCB_549 Validation 6.87 8.24 8.65 12.15 −6.00 GCB_550 Validation 8.98 8.29 8.76 12.24 −6.94 GCB_553 Validation 8.51 8.64 8.62 12.63 −6.69 GCB_565 Validation 7.97 8.79 9.79 13.42 −6.98 GCB_572 Training 7.61 8.60 9.39 12.58 −6.42 GCB_617 Validation 8.31 7.89 7.54 13.17 −6.12 GCB_618 Training 5.66 8.97 9.20 13.32 −5.54 GCB_619 Validation 7.83 8.65 9.34 12.12 −6.36 GCB_623 Training 7.16 8.88 9.26 12.35 −6.21 GCB_627 Validation 8.13 8.83 8.62 11.85 −6.31 GCB_654 Training 6.30 9.60 8.45 10.00 −4.88 GCB_661 Validation 8.46 8.51 8.18 12.66 −6.33 GCB_669 Training 7.88 8.65 8.59 12.32 −6.19 GCB_672 Training 8.29 8.61 8.14 12.41 −6.21 GCB_674 Validation 8.36 8.62 7.76 12.33 −6.14 GCB_675 Validation 6.01 9.52 8.90 10.12 −5.09 GCB_681 Training 9.25 8.72 8.72 12.59 −6.89 GCB_688 Validation 6.97 9.01 9.90 9.94 −5.99 GCB_695 Validation 8.80 8.73 9.23 12.45 −6.84 GCB_698 Validation 9.27 8.35 8.85 11.99 −6.96 GCB_701 Training 7.77 7.93 8.68 13.10 −6.33 GCB_710 Validation 6.12 8.78 7.65 13.19 −5.24 GCB_711 Training 7.57 8.80 8.43 11.44 −5.84 GCB_722 Training 7.78 8.31 8.93 12.61 −6.51 GCB_724 Training 7.88 9.08 8.74 11.53 −6.21 GCB_731 Validation 7.72 8.92 9.08 12.20 −6.46 GCB_742 Validation 8.33 8.55 8.58 12.95 −6.70 GCB_744 Training 8.02 8.64 9.36 11.85 −6.52 GCB_745 Training 8.47 8.34 8.93 11.95 −6.67 GCB_747 Validation 7.64 8.48 8.32 13.06 −6.27 GCB_749 Training 7.57 8.61 9.40 12.55 −6.56 GCB_758 Validation 5.66 8.77 7.89 12.51 −4.63 GCB_772 Validation 8.52 7.81 7.95 12.25 −6.34 GCB_777 Validation 7.52 8.65 8.57 11.69 −6.10 GCB_792 Training 8.14 8.64 9.21 12.08 −6.65 GCB_795 Validation 9.19 8.17 8.81 11.60 −6.92 GCB_797 Validation 7.50 8.62 8.08 12.84 −6.09 GCB_803 Validation 6.19 8.65 9.49 13.18 −6.11 GCB_810 Training 8.46 8.32 8.10 13.13 −6.50 GCB_817 Training 6.93 8.51 9.49 11.09 −6.04 GCB_818 Training 7.18 8.96 8.08 12.23 −5.76 GCB_819 Validation 7.16 8.97 8.06 13.22 −5.79 GCB_821 Validation 8.13 8.59 8.90 12.41 −6.61 GCB_832 Training 7.83 8.35 8.71 12.47 −6.37 GCB_836 Validation 7.84 8.99 8.50 11.46 −5.85 GCB_840 Training 8.24 7.75 7.40 11.74 −5.77 GCB_847 Training 7.82 8.17 8.97 12.55 −6.51 GCB_860 Training 7.12 8.39 9.34 11.54 −6.10 GCB_871 Training 5.59 9.60 7.28 11.16 −4.23 GCB_874 Training 8.53 9.14 8.95 11.65 −6.47 GCB_995 Validation 6.98 8.68 8.54 12.22 −5.76 PMBL_1006 Validation 7.34 8.51 7.66 10.94 −5.33 PMBL_1024 Validation 7.62 8.48 8.56 10.89 −5.96 PMBL_1048 Validation 8.68 8.16 7.23 12.18 −6.08 PMBL_1053 Training 7.02 8.28 8.24 11.12 −5.31 PMBL_484 Training 7.15 8.45 7.01 13.62 −5.41 PMBL_546 Validation 8.19 7.88 7.66 11.73 −6.06 PMBL_570 Training 9.34 8.21 8.48 12.70 −6.86 PMBL_621 Training 8.08 8.60 9.14 12.96 −6.72 PMBL_638 Training 7.56 8.26 8.00 11.37 −5.75 PMBL_691 Validation 6.48 8.92 8.40 10.17 −5.04 PMBL_791 Validation 7.72 8.65 8.94 11.56 −6.16 PMBL_824 Validation 8.06 8.01 7.76 13.28 −6.11 PMBL_994 Training 9.15 8.36 7.46 12.43 −6.29 PMBL_998 Training 6.70 8.35 9.24 13.19 −6.20 UC_DLBCL_1001 Validation 6.74 8.43 7.10 12.76 −5.31 UC_DLBCL_1004 Validation 7.54 8.75 8.01 13.09 −6.10 UC_DLBCL_1007 Training 9.97 8.44 7.64 12.97 −6.85 UC_DLBCL_1018 Training 6.42 8.38 6.97 12.71 −5.03 UC_DLBCL_1041 Validation 5.76 8.69 6.78 13.38 −4.71 UC_DLBCL_1054 Training 8.92 8.65 8.51 11.48 −6.59 UC_DLBCL_306 Validation 7.85 8.90 8.31 12.36 −6.23 UC_DLBCL_310 Training 8.14 8.80 7.63 12.27 −6.03 UC_DLBCL_449 Validation 9.03 8.48 7.07 12.17 −6.01 UC_DLBCL_458 Training 5.92 8.53 8.28 9.60 −4.96 UC_DLBCL_460 Validation 7.92 9.08 8.30 12.29 −6.13 UC_DLBCL_491 Training 7.65 8.33 7.35 12.39 −5.53 UC_DLBCL_528 Validation 6.99 8.56 7.36 11.63 −5.35 UC_DLBCL_615 Validation 7.11 8.32 8.77 12.80 −6.10 UC_DLBCL_625 Training 8.93 7.78 7.85 12.62 −6.46 UC_DLBCL_664 Training 7.62 8.15 8.17 12.72 −6.04 UC_DLBCL_671 Training 8.09 8.48 7.61 11.53 −5.78 UC_DLBCL_682 Training 7.38 8.35 7.14 12.33 −5.43 UC_DLBCL_683 Training 7.91 8.36 7.78 12.57 −6.02 UC_DLBCL_684 Validation 8.06 8.63 8.29 12.76 −6.29 UC_DLBCL_748 Validation 5.38 8.57 7.45 9.55 −4.23 UC_DLBCL_751 Training 6.33 8.65 8.88 13.14 −5.74 UC_DLBCL_808 Training 7.42 9.01 7.44 13.09 −5.63 UC_DLBCL_831 Validation 8.33 8.30 7.46 11.58 −5.84 UC_DLBCL_834 Training 6.98 9.09 8.61 11.77 −5.66 UC_DLBCL_838 Validation 7.25 8.40 7.23 12.56 −5.36 UC_DLBCL_851 Validation 6.28 9.05 6.78 8.19 −4.10 UC_DLBCL_854 Validation 7.36 8.50 7.39 12.59 −5.53 UC_DLBCL_855 Training 8.31 7.94 7.49 12.08 −6.07 UC_DLBCL_856 Validation 5.65 9.01 8.52 9.32 −4.68

In order to visualize the predictive power of the model, the 200 samples were ranked according to their survival predictor scores and divided into four quartiles. Kaplan-Meier plots of overall survival probability show clear differences in survival rate between these four quartiles (FIG. 11).

Example 8 Development of a Third DLBCL Survival Predictor Using Gene Expression Data from the Lymph Dx Microarray

The number of genes used to generate the DLBCL survival predictor in Example 7 were reduced in order to create a survival predictor compatible with RT-PCR. The list of genes from the lymph node and germinal center B-cell gene expression signatures was narrowed to those three genes from each signature that were most closely correlated with the lymph node and germinal center B-cell gene expression signature values, respectively. The genes from the proliferation gene expression signature did not add significantly to the reduced gene survival prediction model, so they were removed entirely. The expression of the genes within each signature was averaged on the log₂ scale to generate a gene expression signature value for each signature. Table 2374 lists the genes that were used to generate these gene expression signature values.

TABLE 2374 Unigene ID Build Signature UNIQID 167 Gene symbol Germinal center B-cell 1099686 117721 Germinal center B-cell 1529318 291954 Germinal center B-cell 1529344 317970 SERPINA11 Lymph node 1097126 274520 ANTXR1 Lymph node 1099358 93135 Lymph node 1121029 412999 CSTA MHC class II 1136777 387679 HLA-DQA1 MHC class II 1136877 409934 HLA-DQB1

Table 2375 lists p-values for the association of each signature with survival in the training set, the validation set, and overall.

TABLE 2375 Signature Training set Validation set Overall Lymph node 6.1 × 10⁻⁶ 0.0021  2.1 × 10⁻¹⁷ Germinal center B-cell 3.5 × 10⁻⁴ 0.0099 2.7 × 10⁻⁵ MHC class II 0.024 0.0026 0.00031

The three gene expression signatures were used to generate a survival predictor score using the following equation: Survival predictor score=[−0.32*(lymph node gene expression signature value)]−[0.176*(germinal center B-cell gene expression signature value)]−[0.206*(MHC class II gene expression signature value)]. A higher survival predictor score was associated with worse outcome. For the 200 DLBCL samples used to generate the model, the survival predictor score had a mean of 6.54 and a standard deviation of 0.69, with each unit increase in the predictor score corresponding to an approximately 2.7 fold increase in the relative risk of death. Data for all 200 samples is presented in Table 2376.

TABLE 2376 Germinal MHC class Lymph node center B-cell II Survival signature signature signature predictor Sample ID # Set value value value score ABC_1000 Validation 8.08 5.68 11.50 −5.96 ABC_1002 Validation 8.32 6.06 12.54 −6.31 ABC_1023 Validation 9.36 4.74 11.42 −6.18 ABC_1027 Training 7.41 4.90 12.31 −5.77 ABC_1031 Validation 9.40 5.23 11.68 −6.33 ABC_1034 Validation 7.47 4.92 11.83 −5.69 ABC_1038 Training 7.89 5.84 12.30 −6.09 ABC_1043 Training 7.84 4.66 12.29 −5.86 ABC_1045 Validation 9.31 4.66 12.07 −6.29 ABC_1055 Validation 6.46 6.38 13.05 −5.88 ABC_1057 Training 9.13 7.93 12.05 −6.80 ABC_1059 Validation 10.93 4.82 11.35 −6.68 ABC_1061 Training 8.18 5.04 12.28 −6.04 ABC_304 Validation 7.31 6.47 12.76 −6.10 ABC_305 Training 7.02 6.60 11.89 −5.86 ABC_309 Validation 10.47 7.00 12.53 −7.16 ABC_413 Validation 7.99 4.80 9.04 −5.26 ABC_428 Training 9.43 7.59 10.25 −6.47 ABC_432 Validation 7.29 8.16 9.56 −5.74 ABC_446 Training 9.49 5.46 10.55 −6.17 ABC_462 Validation 7.72 4.97 13.36 −6.10 ABC_477 Validation 7.16 3.69 12.45 −5.51 ABC_481 Training 9.75 6.89 11.98 −6.80 ABC_482 Training 10.51 7.64 12.35 −7.25 ABC_538 Validation 8.79 5.00 11.83 −6.13 ABC_541 Training 7.70 5.80 10.59 −5.67 ABC_544 Training 8.90 3.98 11.87 −5.99 ABC_547 Validation 7.05 5.18 11.38 −5.51 ABC_577 Validation 9.93 8.05 11.95 −7.06 ABC_616 Validation 7.34 4.54 12.64 −5.75 ABC_626 Validation 8.78 6.77 11.11 −6.29 ABC_633 Training 9.63 5.02 12.45 −6.53 ABC_642 Training 7.31 4.95 13.80 −6.05 ABC_644 Validation 7.72 5.35 13.28 −6.15 ABC_645 Training 9.77 6.21 13.39 −6.98 ABC_646 Validation 7.39 3.75 11.60 −5.41 ABC_652 Validation 7.51 4.53 12.73 −5.82 ABC_660 Training 5.85 3.55 10.17 −4.59 ABC_663 Training 7.04 5.06 12.82 −5.78 ABC_668 Validation 8.00 5.65 10.57 −5.73 ABC_676 Training 6.53 4.29 13.32 −5.59 ABC_678 Training 6.87 7.48 11.26 −5.83 ABC_687 Validation 6.39 3.78 10.46 −4.87 ABC_689 Training 8.29 5.07 12.56 −6.13 ABC_692 Validation 8.10 5.26 11.57 −5.90 ABC_694 Validation 9.67 8.15 12.41 −7.09 ABC_700 Training 8.37 6.75 12.10 −6.36 ABC_702 Validation 8.44 4.59 12.55 −6.09 ABC_704 Training 8.51 4.34 12.83 −6.13 ABC_709 Validation 7.47 4.54 13.40 −5.95 ABC_712 Validation 7.12 3.99 12.02 −5.46 ABC_714 Training 9.57 7.03 11.97 −6.77 ABC_717 Training 8.33 5.54 11.34 −5.98 ABC_725 Training 8.04 4.40 12.76 −5.97 ABC_726 Validation 7.79 4.18 11.91 −5.68 ABC_730 Validation 8.13 7.36 12.14 −6.40 ABC_753 Training 9.24 6.60 13.00 −6.80 ABC_756 Training 9.51 5.21 12.48 −6.53 ABC_771 Training 8.08 4.74 12.20 −5.93 ABC_779 Training 8.11 4.09 9.82 −5.34 ABC_800 Validation 10.34 4.83 11.91 −6.61 ABC_807 Training 6.58 4.44 7.56 −4.44 ABC_809 Training 9.29 5.72 10.83 −6.21 ABC_816 Training 6.36 6.36 10.64 −5.35 ABC_820 Training 8.10 4.79 11.98 −5.90 ABC_823 Validation 6.63 4.85 10.09 −5.05 ABC_835 Validation 9.17 7.78 12.31 −6.84 ABC_839 Training 8.06 4.97 11.89 −5.90 ABC_841 Validation 8.05 6.24 13.19 −6.39 ABC_858 Training 9.02 4.86 11.74 −6.16 ABC_872 Training 8.67 5.85 12.47 −6.37 ABC_875 Training 9.60 5.59 11.26 −6.37 ABC_912 Validation 7.99 7.74 12.79 −6.56 ABC_996 Validation 6.89 6.23 10.02 −5.36 GCB_1005 Validation 9.02 9.56 13.27 −7.30 GCB_1008 Training 9.27 10.49 12.83 −7.46 GCB_1009 Training 7.80 10.09 12.28 −6.80 GCB_1021 Validation 8.73 9.20 13.20 −7.13 GCB_1025 Validation 9.94 9.97 12.37 −7.49 GCB_1026 Training 9.54 10.20 13.52 −7.63 GCB_1037 Training 6.34 8.79 12.57 −6.17 GCB_1039 Training 8.71 9.94 13.28 −7.27 GCB_1049 Validation 10.53 8.18 12.60 −7.41 GCB_1051 Validation 7.63 10.18 12.76 −6.86 GCB_1058 Validation 8.61 9.04 12.80 −6.98 GCB_1060 Validation 10.23 9.38 12.96 −7.59 GCB_412 Training 8.79 7.92 13.09 −6.90 GCB_415 Training 10.72 8.57 11.99 −7.41 GCB_421 Training 9.23 5.26 12.20 −6.39 GCB_424 Training 11.14 8.46 12.44 −7.62 GCB_433 Training 9.26 8.52 12.64 −7.07 GCB_434 Training 9.73 10.13 12.54 −7.48 GCB_438 Validation 9.60 9.99 12.51 −7.41 GCB_459 Validation 10.51 7.75 11.37 −7.07 GCB_470 Validation 9.56 6.63 12.23 −6.74 GCB_479 Validation 7.77 4.71 13.07 −6.01 GCB_492 Training 8.82 9.52 12.34 −7.04 GCB_517 Validation 9.92 6.96 12.76 −7.03 GCB_523 Training 6.59 9.17 12.77 −6.35 GCB_524 Training 10.00 7.83 12.51 −7.16 GCB_529 Training 5.61 7.93 10.77 −5.41 GCB_533 Training 9.55 5.54 12.44 −6.59 GCB_537 Validation 8.25 10.25 13.29 −7.18 GCB_543 Validation 9.92 8.85 12.06 −7.21 GCB_545 Training 9.69 4.91 12.90 −6.62 GCB_549 Validation 7.86 8.88 12.15 −6.58 GCB_550 Validation 10.64 9.53 12.24 −7.60 GCB_553 Validation 10.14 9.05 12.63 −7.44 GCB_565 Validation 9.08 10.80 13.42 −7.57 GCB_572 Training 8.93 10.03 12.58 −7.21 GCB_617 Validation 9.27 7.80 13.17 −7.05 GCB_618 Training 7.23 9.11 13.32 −6.66 GCB_619 Validation 9.63 9.63 12.12 −7.27 GCB_623 Training 8.94 9.07 12.35 −7.00 GCB_627 Validation 9.72 8.33 11.85 −7.02 GCB_654 Training 7.04 5.60 10.00 −5.30 GCB_661 Validation 10.27 7.92 12.66 −7.29 GCB_669 Training 9.15 9.29 12.32 −7.10 GCB_672 Training 9.69 7.36 12.41 −6.95 GCB_674 Validation 9.93 6.23 12.33 −6.81 GCB_675 Validation 7.48 8.46 10.12 −5.97 GCB_681 Training 10.77 9.52 12.59 −7.72 GCB_688 Validation 8.01 10.17 9.94 −6.40 GCB_695 Validation 10.58 9.38 12.45 −7.60 GCB_698 Validation 10.44 9.00 11.99 −7.39 GCB_701 Training 9.38 9.27 13.10 −7.33 GCB_710 Validation 6.96 5.59 13.19 −5.93 GCB_711 Training 9.28 8.49 11.44 −6.82 GCB_722 Training 8.93 9.51 12.61 −7.13 GCB_724 Training 9.51 8.39 11.53 −6.90 GCB_731 Validation 8.82 9.19 12.20 −6.95 GCB_742 Validation 9.95 9.37 12.95 −7.50 GCB_744 Training 10.23 10.11 11.85 −7.49 GCB_745 Training 10.29 9.71 11.95 −7.46 GCB_747 Validation 9.83 9.79 13.06 −7.56 GCB_749 Training 8.57 10.27 12.55 −7.14 GCB_758 Validation 6.88 5.69 12.51 −5.78 GCB_772 Validation 9.92 7.28 12.25 −6.98 GCB_777 Validation 9.03 9.63 11.69 −6.99 GCB_792 Training 9.49 9.06 12.08 −7.12 GCB_795 Validation 11.12 9.02 11.60 −7.54 GCB_797 Validation 8.42 5.90 12.84 −6.38 GCB_803 Validation 7.33 10.11 13.18 −6.84 GCB_810 Training 10.00 8.22 13.13 −7.35 GCB_817 Training 8.60 10.16 11.09 −6.82 GCB_818 Training 9.14 7.78 12.23 −6.81 GCB_819 Validation 9.08 8.63 13.22 −7.15 GCB_821 Validation 10.05 9.81 12.41 −7.50 GCB_832 Training 8.83 6.91 12.47 −6.61 GCB_836 Validation 9.49 7.86 11.46 −6.78 GCB_840 Training 9.45 5.02 11.74 −6.33 GCB_847 Training 9.41 8.77 12.55 −7.14 GCB_860 Training 9.02 6.66 11.54 −6.43 GCB_871 Training 6.60 4.46 11.16 −5.20 GCB_874 Training 10.39 9.13 11.65 −7.33 GCB_995 Validation 8.52 9.35 12.22 −6.89 PMBL_1006 Validation 8.72 4.67 10.94 −5.86 PMBL_1024 Validation 9.30 8.47 10.89 −6.71 PMBL_1048 Validation 10.30 4.98 12.18 −6.68 PMBL_1053 Training 8.75 9.78 11.12 −6.81 PMBL_484 Training 8.25 4.96 13.62 −6.32 PMBL_546 Validation 9.66 6.07 11.73 −6.57 PMBL_570 Training 10.58 8.54 12.70 −7.50 PMBL_621 Training 9.39 9.94 12.96 −7.43 PMBL_638 Training 9.81 8.35 11.37 −6.95 PMBL_691 Validation 8.37 7.51 10.17 −6.10 PMBL_791 Validation 9.29 8.65 11.56 −6.88 PMBL_824 Validation 9.87 7.19 13.28 −7.16 PMBL_994 Training 11.27 6.73 12.43 −7.35 PMBL_998 Training 7.92 8.34 13.19 −6.72 UC_DLBCL_1001 Validation 8.25 5.63 12.76 −6.26 UC_DLBCL_1004 Validation 9.01 7.01 13.09 −6.81 UC_DLBCL_1007 Training 11.42 6.73 12.97 −7.51 UC_DLBCL_1018 Training 7.77 4.58 12.71 −5.91 UC_DLBCL_1041 Validation 7.90 4.33 13.38 −6.05 UC_DLBCL_1054 Training 10.41 8.72 11.48 −7.23 UC_DLBCL_306 Validation 9.42 6.54 12.36 −6.71 UC_DLBCL_310 Training 9.97 5.50 12.27 −6.69 UC_DLBCL_449 Validation 10.01 5.37 12.17 −6.65 UC_DLBCL_458 Training 7.50 5.79 9.60 −5.40 UC_DLBCL_460 Validation 10.26 8.27 12.29 −7.27 UC_DLBCL_491 Training 9.43 4.73 12.39 −6.40 UC_DLBCL_528 Validation 8.42 6.19 11.63 −6.18 UC_DLBCL_615 Validation 8.44 9.01 12.80 −6.92 UC_DLBCL_625 Training 10.43 8.27 12.62 −7.39 UC_DLBCL_664 Training 9.80 8.74 12.72 −7.29 UC_DLBCL_671 Training 9.42 5.26 11.53 −6.32 UC_DLBCL_682 Training 9.01 4.73 12.33 −6.26 UC_DLBCL_683 Training 8.85 8.23 12.57 −6.87 UC_DLBCL_684 Validation 9.62 8.78 12.76 −7.25 UC_DLBCL_748 Validation 7.60 5.79 9.55 −5.42 UC_DLBCL_751 Training 6.40 9.91 13.14 −6.50 UC_DLBCL_808 Training 9.44 7.01 13.09 −6.95 UC_DLBCL_831 Validation 9.45 5.81 11.58 −6.43 UC_DLBCL_834 Training 8.52 7.66 11.77 −6.50 UC_DLBCL_838 Validation 8.49 4.60 12.56 −6.11 UC_DLBCL_851 Validation 7.50 4.82 8.19 −4.94 UC_DLBCL_854 Validation 8.35 5.82 12.59 −6.29 UC_DLBCL_855 Training 9.56 5.44 12.08 −6.51 UC_DLBCL_856 Validation 6.81 7.49 9.32 −5.42

In order to visualize the predictive power of the model, the 200 samples were ranked according to their survival predictor scores and divided into four quartiles. Kaplan-Meier plots of overall survival probability show clear differences in survival rate between these four quartiles (FIG. 12).

Example 9 Development of an MCL Survival Predictor Using Gene Expression Data from Affymetrix U133A and U133B Microarrays

The connection between higher expression of proliferation genes and worse survival in MCL had previously been documented and validated (Rosenwald 2003). A cluster of proliferation genes had been identified in the DLBCL samples used to create the DLBCL survival predictor described in Example 7. By averaging the expression of these genes, a proliferation gene expression signature value had been developed for the DLBCL samples. The correlation of this signature with each probe set on the U133A and U133B microarrays was determined, and the 22 genes for which the correlation was greater than 0.5 were labeled proliferation genes. The correlation between expression of these proliferation genes and survival in 21 MCL samples was estimated using the Cox proportional hazards model. Table 2377 lists these 21 MCL samples.

TABLE 2377 Length of follow-up Status at Used in creating Sample ID # (years) follow-up survival predictor? MCL_1012 3.19 Alive Yes MCL_1091 3.03 Alive Yes MCL_1114 0.59 Dead Yes MCL_1128 0.43 Dead Yes MCL_1150 3.21 Dead Yes MCL_1162 0.78 Alive Yes MCL_1166 0.53 Dead Yes MCL_1194 0.55 Alive Yes MCL_885 1.19 Alive Yes MCL_918 1.95 Dead Yes MCL_924 5.48 Dead Yes MCL_925 7.23 Alive Yes MCL_926 5.18 Dead Yes MCL_936 2.80 Alive Yes MCL_939 1.07 Dead Yes MCL_953 2.31 Dead Yes MCL_956 1.40 Dead Yes MCL_964 0.75 Alive Yes MCL_966 0.21 Dead Yes MCL_968 1.59 Dead Yes MCL_970 5.02 Dead Yes Out of the 22 proliferation genes, 11 were significant at a 0.001 level. The expression level of these 11 genes in each of the 21 MCL samples was averaged to generate a proliferation gene expression signature value. No other genes represented on the U133A or U133B microarrays correlated with MCL survival to an extent greater than would be expected by chance, so the final model included only proliferation genes. The 11 genes used to generate the model are presented in Table 2378.

TABLE 2378 Signature UNIQID Gene Symbol Proliferation 1097290 CIRH1A Proliferation 1101295 FLJ40629 Proliferation 1119729 TK1 Proliferation 1120153 LMNB1 Proliferation 1120494 CDC6 Proliferation 1124745 KIAA0056 Proliferation 1126148 DKFZp586E1120 Proliferation 1130618 TPI1 Proliferation 1134753 WHSC1 Proliferation 1139654 ECT2 Proliferation 1140632 IMAGE:52707

A survival predictor score for MCL was generated using the following equation: Survival predictor score=1.66*(proliferation gene expression signature value). This model was associated with survival in a statistically significant manner (p=0.00018). To illustrate the significance of the model in predicting survival, the 21 MCL samples were divided into two equivalent groups based on their survival predictor scores. Those samples with survival predictor scores above the median were placed in the high proliferation group, while those with survival predictor scores below the median were placed in the low proliferation group. FIG. 13 illustrates the Kaplan Meier survival estimates for these two groups. Median survival for the high proliferation group was 1.07 years, while median survival for the low proliferation group was 5.18 years.

Example 10 Development of an MCL Survival Predictor Using Gene Expression Data from the Lymph Dx Microarray

A set of 21 genes associated with proliferation and poor prognosis in MCL had been identified previously (Rosenwald 2003). Of these 21 genes, only four were represented on the Lymph Dx microarray. In order to find a larger set of genes on the Lymph Dx microarray associated with survival in MCL, Lymphochip expression data (Rosenwald 2003) was re-analyzed and another set of proliferation genes whose expression levels were correlated with poor survival in MCL were identified. Thirteen of these genes were represented on the Lymph Dx microarray (median expression>6 on log₂ scale). These 13 genes are listed in Table 2379.

TABLE 2379 Signature UNIQID Unigene ID Build 167 Gene symbol Proliferation 1119294 156346 TOP2A Proliferation 1119729 164457 TK1 Proliferation 1120153 89497 LMNB1 Proliferation 1121276 24529 CHEK1 Proliferation 1123358 442658 AURKB Proliferation 1124178 446579 HSPCA Proliferation 1124563 249441 WEE1 Proliferation 1130799 233952 PSMA7 Proliferation 1131274 374378 CKS1B Proliferation 1131778 396393 UBE2S Proliferation 1132449 250822 STK6 Proliferation 1135229 367676 DUT Proliferation 1136585 80976 MKI67

The expression levels of the 13 genes listed in Table 2379 on the Lymph Dx microarray were transformed into the log₂ scale and averaged to form a proliferation gene expression signature value. This was used to generate a survival predictor score using the following equation: Survival predictor score=1.66*(proliferation gene expression signature value)

For the 21 MCL samples analyzed, the survival predictor score had a mean of 14.85 and a standard deviation of 1.13. Even in this limited sample set, the survival predictor score was significantly associated with prognosis (p=0.0049), with each unit increase in the score corresponding to a 2.7 fold increase in the relative risk of death. Data for all 21 samples is shown in Table 2380.

TABLE 2380 Proliferation Survival predictor Sample ID # signature value score MCL_1012 8.83 14.658 MCL_1091 8.81 14.625 MCL_1114 10.39 17.247 MCL_1128 10.12 16.799 MCL_1150 8.33 13.828 MCL_1162 8.15 13.529 MCL_1166 9.40 15.604 MCL_1194 7.44 12.350 MCL_885 8.68 14.409 MCL_918 9.33 15.488 MCL_924 8.35 13.861 MCL_925 8.86 14.708 MCL_926 8.14 13.512 MCL_936 8.56 14.21 MCL_939 9.14 15.172 MCL_953 9.25 15.355 MCL_956 9.35 15.521 MCL_964 9.74 16.168 MCL_966 8.76 14.542 MCL_968 9.10 15.106 MCL_970 9.27 15.388

To illustrate the significance of the model in predicting survival, the 21 MCL samples were divided into two equivalent groups based on their survival predictor scores. Those samples with survival predictor scores above the median were placed in the high proliferation group, while those with survival predictor scores below the median were placed in the low proliferation group. FIG. 14 illustrates the Kaplan Meier survival estimates for these two groups.

Example 11 Identification of Lymphoma Samples as MCL Based on Bayesian Analysis of Gene Expression Data from Affymetrix U133A and U133B Microarrays

A statistical method based on Bayesian analysis was developed to distinguish MCL samples from samples belonging to other lymphoma types based on gene expression profiling. This method was developed using the gene expression data obtained in Example 1 for the following lymphoma types: ABC, GCB, PMBL, BL, FH, FL, MALT, MCL, PTLD, SLL, and splenic marginal zone lymphoma (splenic). Tables 1707-1741 (discussed in Example 1) provide gene expression data for samples within each of these lymphoma types, including the expression level of each gene and the difference in expression of each gene between types. Tables 1710, 1715, and 1723 (corresponding to FL, MCL, and DLBCL, respectively) include the correlation between expression of each gene and survival.

To determine the lymphoma type of a sample, a series of predictor models are generated. Each predictor model calculates the probability that the sample belongs to a first lymphoma type rather than a second lymphoma type. A method was developed to determine whether a sample was MCL, or one of the following lymphoma types: ABC, BL, FH, FL, GCB, MALT, PMBL, PTLD, SLL, or splenic. This method required ten different predictor models, each designed to determine whether the sample belonged to MCL or one of the other ten lymphoma types (e.g., MCL vs. ABC, MCL vs. BL, etc.).

Several of the lymphoma samples analyzed displayed a tendency towards elevated or reduced expression of genes from the lymph node and proliferation gene expression signatures. These genes are likely to be highly differentially expressed between the lymphoma types, but they do not serve as good predictor genes because they are often variably expressed within a single lymphoma type. For this reason, any gene that displayed a correlation with the proliferation or lymph node signatures was eliminated from consideration.

For each lymphoma type pair (e.g., MCL vs. ABC, MCL vs. FL, etc.), 20 genes were identified that exhibited the greatest difference in expression between MCL and the second lymphoma type according to a Student's t-test. The choice to use 20 genes was arbitrary. For each sample X, the 20 genes were used to generate a linear predictor score (LPS) according to the following formula:

${{{LPS}(X)} = {\sum\limits_{j = 1}^{20}{t_{j}X_{j}}}},$ where X_(j) is the expression of gene j in sample X and t_(j) is the t-statistic for the difference in expression of gene j between a first lymphoma type and a second lymphoma type. This is merely one method for generating an LPS. Others methods include linear discriminant analysis (Dudoit 2002), support vector machines (Furey 2000), or shrunken centroids (Tibshirani 2002). In addition, there is no requirement that a t-statistic be used as the scaling factor.

After an LPS had been formulated for each lymphoma sample, the mean and standard deviation of these LPS's was calculated for each lymphoma type. For a new sample X, Bayes' rule can be used to estimate the probability that the sample belongs to a first lymphoma type rather than a second lymphoma type (FIG. 15). In this example, Bayes' rule was used to calculate the probability q that sample X was MCL rather than a second lymphoma type using the following equation:

${q\left( {X\mspace{14mu}{is}\mspace{14mu}{type}\mspace{14mu} 1} \right)} = \frac{\phi\left( {{{{LPS}(X)};{\hat{\mu}}_{1}},{\hat{\sigma}}_{1}} \right)}{{\phi\left( {{{{LPS}(X)};{\hat{\mu}}_{1}},{\hat{\sigma}}_{1}} \right)} + {\phi\left( {{{{LPS}(X)};{\hat{\mu}}_{2}},{\hat{\sigma}}_{2}} \right)}}$ where type 1 is MCL, type 2 is one of the other nine lymphoma types, φ(x; μ, σ) is the normal density function with mean μ and standard deviation σ, {circumflex over (μ)}₁ and {circumflex over (σ)}₁ are the sample mean and variance of the LPS values for lymphoma type 1, and {circumflex over (μ)}₂ and {circumflex over (σ)}₂ are the sample mean and variance of the LPS values for lymphoma type 2.

This method was used to develop ten predictor models, one for each pairing of MCL and a second lymphoma type. A sample was classified as MCL if each of the ten predictors generated at least a 90% probability that the sample was MCL. If any of the ten predictors indicated a probability of less than 90%, the sample was classified as non-MCL.

The 10 sets of 20 genes that were included in these models and the t-statistics for each gene are presented in Tables 2381-2490.

TABLE 2381 MCL vs. ABC predictor genes Gene GENBANK Affymetrix probe UNIQID symbol gi Accession set ID Scale Factor 1103711 10433184 AK021895.1 232478_at 17.88496416 1133111 PDE9A 48762717 NM_001001567.1 205593_s_at 17.61579873 1137987 PLXNB1 41152087 NM_002673.3 215807_s_at 17.47030156 1132835 SOX11 30581115 NM_003108.3 204915_s_at 16.89404131 1109505 MGC39372 19263721 BC025340.1 239186_at 15.78111902 1139054 ZBED5 49574209 NM_021211.2 218263_s_at 15.77800815 1119361 TIA1 11863160 NM_022037.1 201448_at 15.68070962 1115226 KIAA1683 144922706 NM_025249.2 223600_s_at 15.67954057 1101211 STRBP 21361744 NM_018387.2 229513_at 15.4183527 1118963 11084506 BF196503.1 65472_at 15.36802586 1096503 C9orf45 12005727 AF251293.1 223522_at 14.64776335 1127849 SNN 83627728 NM_003498.4 218032_at 14.54859775 1099204 5817123 AL110204.1 227121_at 14.32724822 1098840 CCDC50 41281910 NM_178335.1 226713_at 14.10346944 1139444 RABL2B 51317348 NM_001003789.1 219151_s_at 14.10016196 1106855 KIAA1909 148529024 NM_052909.3 236255_at 13.9504946 1126695 LOC730273 113419660 XM_001124203.1 216000_at 13.92285415 1120137 FCGBP 154146261 NM_003890.2 203240_at 13.86147896 1133011 TMSL8 72255577 NM_021992.2 205347_s_at 13.74377784 1133192 RASGRP3 24762238 NM_170672.1 205801_s_at −17.09085725

TABLE 2382 MCL vs. BL predictor genes Gene GENBANK Affymetrix probe UNIQID symbol gi Accession set ID Scale Factor 1120900 EPHB6 56119211 NM_004445.2 204718_at 13.43582327 1112061 SDK2 48255893 NM_019064.3 242064_at 12.73065392 1109505 MGC39372 19263721 BC025340.1 239186_at 12.63674985 1133099 DNASE1L3 58331226 NM_004944.2 205554_s_at 12.43333984 1106855 KIAA1909 148529024 NM_052909.3 236255_at 12.32623489 1110070 8168250 AW977010.1 239803_at 12.05416064 1121739 ZNF135 34419632 NM_003436.2 206142_at 11.90460363 1098840 CCDC50 41281910 NM_178335.1 226713_at 11.90309143 1132833 SOX11 30581115 NM_003108.3 204913_s_at 11.60864812 1121693 PLCH2 78499632 NM_014638.2 206080_at 11.33634052 1123760 LILRA4 47519952 NM_012276.3 210313_at 11.18744726 1125964 TMEM63A 7662307 NM_014698.1 214833_at 11.14762675 1112306 6139540 AW135407.1 242354_at 11.02434114 1096070 DNMT3A 28559066 NM_022552.3 222640_at 10.98991879 1129943 ZNF506 149944539 NM_001099269.1 221626_at 10.72494956 1118749 11125347 AJ296290.1 39313_at 10.64623382 1098954 MOBKL2B 41350329 NM_024761.3 226844_at 10.46164401 1134749 ZMYND8 34335265 NM_012408.3 209049_s_at 10.40948157 1131860 BIN1 21536381 NM_004305.2 202931_x_at 10.31084561 1123148 TGFBR2 133908632 NM_001024847.2 208944_at 10.2956213

TABLE 2383 MCL vs. FH predictor genes Gene GENBANK Affymetrix probe UNIQID symbol gi Accession set ID Scale Factor 1132834 SOX11 30581115 NM_003108.3 204914_s_at 24.3531072 1100873 WNT3 21536426 NM_030753.3 229103_at 16.83342764 1109603 4971784 A1694444.1 239292_at 13.02401995 1139411 OSBPL10 23111057 NM_017784.3 219073_s_at 12.54369577 1106855 KIAA1909 148529024 NM_052909.3 236255_at 12.10316361 1125193 CNR1 38683843 NM_016083.3 213436_at 12.070579 1137450 ALOX5 62912458 NM_000698.2 214366_s_at 11.74571823 1100258 KLHL14 55741642 NM_020805.1 228377_at 11.60998697 1133167 ZNF107 62243639 NM_001013746.1 205739_x_at 11.52931491 1136831 PPFIBP2 57163846 NM_003621.1 212841_s_at 11.50062692 1138222 CD24 73623396 NM_013230.2 216379_x_at 10.99674674 1099437 PTPRJ 148728159 NM_001098503.1 227396_at 10.90797288 1140236 FCRL2 74048626 NM_138738.2 221239_s_at 10.77082801 1114109 CLECL1 40548404 NM_172004.2 244413_at 10.65867119 1098277 PRICKLE1 23308518 NM_153026.1 226065_at 10.55457068 1135138 CD24 73623396 NM_013230.2 209771_x_at 10.41999962 1103304 L0C439949 113421296 XM_001128367.1 232001_at −10.46625233 1128460 PITPNC1 32307139 NM_012417.2 219155_at −10.91106245 1121953 KIAA0125 20302136 NM_014792.2 206478_at −11.22466255 1129281 FAM30A 6841343 AF161538.1 220377_at −15.54465448

TABLE 2384 MCL vs. FL predictor genes Gene GENBANK Affymetrix probe UNIQID symbol gi Accession set ID Scale Factor 1132835 SOX11 30581115 NM_003108.3 204915_s_at 22.14208817 1096070 DNMT3A 28559066 NM_022552.3 222640_at 20.53740132 1103711 10433184 AK021895.1 232478_at 20.49880004 1137987 PLXNB1 41152087 NM_002673.3 215807_s_at 18.38081568 1109505 MGC39372 19263721 BC025340.1 239186_at 17.17812448 1098840 CCDC50 41281910 NM_178335.1 226713_at 16.32703666 1130926 C5orf13 4758865 NM_004772.1 201310_s_at 15.34261878 1096396 SPG3A 74024913 NM_015915.3 223340_at 14.75437736 1132734 COL9A3 119508425 NM_001853.3 204724_s_at 14.684583 1139393 OPN3 71999130 NM_014322.2 219032_x_at 14.39118445 1115537 CNFN 26024194 NM_032488.2 224329_s_at 14.18446144 1102215 PRICKLE1 23308518 NM_003105.3 230708_at 14.16246426 1124585 SORL1 18379347 NM_003105.3 212560_at −14.33315955 1137561 HOXA1 84697023 NM_005522.4 214639_s_at −15.38404642 1100581 BCL6 21040323 NM_001706.2 228758_at −15.91666634 1124646 RFTN1 41872576 NM_015150.1 212646_at −16.40577696 1114543 5395779 A1809213.1 244887_at −17.60167863 1120090 BCL6 21040323 NM_001706.2 203140_at −17.63091181 1123731 RGS13 21464137 NM_170672.1 210258_at −22.41602151 1133192 RASGRP3 24762238 NM_170672.1 205801_s_at −27.28308723

TABLE 2385 MCL vs. GCB predictor genes Gene GENBANK Affymetrix probe UNIQID symbol gi Accession set ID Scale Factor 1098840 CCDC50 41281910 NM_178335.1 226713_at 22.26488562 1132835 SOX11 30581115 NM_003108.3 204915_s_at 17.76179754 1137987 PLXNB1 41152087 NM_002673.3 215807_s_at 16.86845147 1098954 MOBKL2B 41350329 NM_024761.3 226844_at 16.65023669 1103711 10433184 AK021895.1 232478_at 15.64719784 1096070 DNMT3A 28559066 NM_022552.3 222640_at 15.22540494 1139393 OPN3 71999130 NM_014322.2 219032_x_at 14.64030565 1127849 SNN 83627728 NM_003498.4 218032_at 14.28242206 1098156 MAP3K1 153945764 NM_005921.1 225927_at 14.00049272 1128845 SIDT1 116812583 NM_017699.2 219734_at 13.96064416 1129943 ZNF506 149944539 NM_001099269.1 221626_at 13.85404507 1140116 ARHGAP24 111154091 NM_001025616.2 221030_s_at 13.81464172 1106855 KIAA1909 148529024 NM_052909.3 236255_at 13.74521849 1120900 EPHB6 56119211 NM_004445.2 204718_at 13.46567004 1127371 4372199 A1479031.1 217164_at 13.45735668 1119361 TIA1 11863160 NM_022037.1 201448_at 13.37376559 1120854 EDG1 87196352 NM_001400.3 204642_at 13.1047657 1098277 PRICKLE1 23308518 NM_153026.1 226065_at 13.04993076 1140127 TRIM6-TRIM34 51477689 NM_001003819.1 221044_s_at 12.66260609 1100581 BCL6 21040323 NM_001706.2 228758_at −12.81251689

TABLE 2386 MCL vs. MALT predictor genes Gene GENBANK Affymetrix probe UNIQID symbol gi Accession set ID Scale Factor 1132834 SOX11 30581115 NM_003108.3 204914_s_at 20.7489202 1101987 K1AA1909 148529024 NM_052909.3 230441_at 10.78991326 1100873 WNT3 21536426 NM_030753.3 229103_at 10.11845036 1130764 HNRNPA0 52426775 NM_006805.3 201055_s_at 9.432459453 1102178 H2BFXP 115432116 NR_003238.1 230664_at 9.035605572 1098277 PRICKLE1 23308518 NM_153026.1 226065_at 9.003360784 1130926 C5orf13 4758865 NM_004772.1 201310_s_at 8.712830747 1098694 SBK1 67906173 NM_001024401.2 226548_at 8.309789856 1103711 10433184 AK021895.1 232478_at 8.248526605 1138099 FADS3 34304362 NM_021727.3 216080_s_at 8.107440225 1120854 EDG1 87196352 NM_001400.3 204642_at 8.045872672 1102215 PRICKLE1 23308518 NM_153026.1 230708_at 8.032351578 1121739 ZNF135 34419632 NM_003436.2 206142_at 8.020919565 1096070 DNMT3A 28559066 NM_022552.3 222640_at 7.964477216 1101211 STRBP 21361744 NM_018387.2 229513_at 7.738742472 1120825 CHL1 27894375 NM_006614.2 204591_at 7.516130116 1099437 PTPRJ 148728159 NM_001098503.1 227396_at 7.209041652 1096503 C9orf45 12005727 AF251293.1 223522_at 7.171540413 1135927 LILRA2 5803067 NM_006866.1 211102_s_at 7.134470829 1120645 FADS3 34304362 NM_021727.3 204257_at 7.039952979

TABLE 2387 MCL vs. PMBL predictor genes Gene GENBANK Affymetrix probe UNIQID symbol gi Accession set ID Scale Factor 1132834 SOX11 30581115 NM_003108.3 204914_s_at 28.17593839 1100873 WNT3 21536426 NM_030753.3 229103_at 17.90004832 1096503 C9orf45 12005727 AF251293.1 223522_at 17.43982729 1098840 CCDC50 41281910 NM_178335.1 226713_at 17.37421052 1124734 ZNF238 45439300 NM_006352.3 212774_at 16.73821457 1135102 PRKCB1 47157320 NM_002738.5 209685_s_at 16.67436366 1103711 10433184 AK021895.1 232478_at 16.57202026 1140416 FAIM3 34147517 NM_005449.3 221601_s_at 15.64802242 1121757 ADRB2 116686129 NM_000024.4 206170_at 15.57336633 1140236 FCRL2 74048626 NM_138738.2 221239_s_at 15.20264513 1099140 28835721 BC047541.1 227052_at 15.11929571 1099549 7154210 AW516128.1 227533_at 14.92883027 1139054 ZBED5 49574209 NM_021211.2 218263_s_at 14.63422275 1138818 ILF3 24234752 NM_004516.2 217804_s_at 14.50621028 1109444 31317247 NM_006850.2 239122_at 14.20430672 1124534 GPATCH8 50962881 NM_001002909.1 212485_at 14.18537487 1098277 PRICKLE1 23308518 NM_153026.1 226065_at 13.98526258 1131687 TLK1 33636697 NM_012290.3 202606_s_at 13.97468703 1125112 PLCL2 142369931 NM_015184.3 213309_at 13.85714318 1125397 RABL4 9257237 NM_006860.2 213784_at 13.85049805

TABLE 2388 MCL vs. PTLD predictor genes Gene GENBANK Affymetrix probe UNIQID symbol gi Accession set ID Scale Factor 1109603 14060141 BG749488.1 239292_at 19.95553782 1138222 CD24 73623396 NM_013230.2 216379_x_at 15.95397369 1135138 CD24 73623396 NM_013230.2 209771_x_at 15.89198725 1134230 RASGRP2 149158726 NM_001098670.1 208206_s_at 15.80452978 1139411 OSBPL10 23111057 NM_017784.3 219073_s_at 14.32818885 1140416 FAIM3 34147517 NM_005449.3 221601_s_at 13.89685188 1132834 SOX11 30581115 NM_003108.3 204914_s_at 13.78424818 1121739 ZNFI35 34419632 NM_003436.2 206142_at 13.02195529 1098156 MAP3K1 153945764 NM_005921.1 225927_at 12.95032505 1099270 AFF3 68348715 NM_001025108.1 227198_at 12.7877735 1139012 MAP4K4 46249361 NM_004834.3 218181_s_at 12.70176225 1120854 EDG1 87196352 NM_001400.3 204642_at 12.25264341 1120985 ARHGAP25 55770897 NM_001007231.1 204882_at 12.04626201 1115952 ATXN1L 21734020 AL833385.1 226095_s_at 11.96299478 1120825 CHL1 27894375 NM_006614.2 204591_at 11.82402907 1131636 SPOCK2 7662035 NM_014767.1 202524_s_at 11.80417657 1136706 PCMTD2 157388996 NM_001104925.1 212406_s_at 11.74962191 1113560 14589868 NM_032966.1 243798_at 11.72049882 1133851 P4HA1 63252885 NM_000917.2 207543_s_at −12.59876059 1137459 BCAT1 72187658 NM_005504.4 214390_s_at −14.00465411

TABLE 2389 MCL vs. SLL predictor genes Gene GENBANK Affymetrix probe UNIQID symbol gi Accession set ID Scale Factor 1132834 SOX11 30581115 NM_003108.3 204914_s_at 23.59602107 1101987 K1AA1909 148529024 NM_052909.3 230441_at 14.50254794 1103711 10433184 AK021895.1 232478_at 13.31375894 1096070 DNMT3A 28559066 NM_022552.3 222640_at 12.37453972 1130926 C5orf13 4758865 NM_004772.1 201310_s_at 11.27840239 1120645 FADS3 34304362 NM_021727.3 204257_at 11.14057287 1138099 FADS3 34304362 NM_021727.3 216080_s_at 10.92729287 1097887 MAST4 148727254 NM_015183.1 225611_at 10.37913127 1099941 15750678 B1759100.1 227985_at 10.33953409 1130373 MAST4 148727254 NM_015183.1 40016_g_at 10.01524528 1110957 SYNE2 118918402 NM_015180.4 240777_at 9.865436185 1130320 MAST4 148727254 NM_015183.1 222348_at 9.807091644 1124373 LPIN1 22027647 NM_145693.1 212274_at 9.024985551 1128813 KREMEN2 27437002 NM_024507.2 219692_at 8.903791941 1131130 MARCKS 153070259 NM_002356.5 201670_s_at 8.688979176 1120825 CHL1 27894375 NM_006614.2 204591_at 8.685132271 1119752 BASPI 30795230 NM_006317.3 202391_at 8.663402838 1131854 GCLC 45359851 NM_001498.2 202923_s_at −8.761521136 1105801 MAN2A1 51477713 NM_002372.2 235103_at −8.828675125 1097824 MAP2 87578393 NM_001039538.1 225540_at −9.345688564

TABLE 2390 MCL vs. splenic predictor genes Gene GENBANK Affymetrix probe UNIQID symbol gi Accession set ID Scale Factor 1106855 KIAA1909 148529024 NM_052909.3 236255_at 14.48278638 1121739 ZNF135 34419632 NM_003436.2 206142_at 11.95918572 1111850 BC047698.1 BC047698.1 241808_at 11.13464157 1098024 RSPRY1 45387948 NM_133368.1 225774_at 10.10869886 1130764 HNRNPA0 52426775 NM_006805.3 201055_s_at 10.06898534 1135342 SHOX2 87044887 NM_003030.3 210135_s_at 9.565884385 1097218 TCEAL8 55749465 NM_001006684.1 224819_at 9.187725705 1117193 ZBTB10 157694498 NM_001105539.1 233899_x_at 9.12522795 1139564 PSMD10 28605122 NM_002814.2 219485_s_at 9.066714773 1132834 SOX11 30581115 NM_003108.3 204914_s_at 8.908574745 1131130 MARCKS 153070259 NM_002356.5 201670_s_at 8.732921026 1131756 PDCD4 34304340 NM_014456.3 202730_s_at 8.441424593 1102187 PKHD1L1 126116588 NM_177531.4 230673_at 8.391861029 1098195 TMEM64 116089278 NM_001008495.2 225974_at 8.349839204 1101211 STRBP 21361744 NM_018387.2 229513_at 8.337208237 1136673 GNAS 117938757 NM_000516.4 212273_x_at 8.254076655 1139116 USP16 50312663 NM_001001992.1 218386_x_at 8.179384251 1098694 SBK1 67906173 NM_001024401.2 226548_at 7.935903681 1120519 WWP2 40806206 NM_007014.3 204022_at −7.881202253 1114916 CPLX3 75677370 NM_001030005.2 222927_s_at −8.33683119

With so many candidate predictor genes being utilized, it is possible to generate a predictor model that accurately predicts every element of a training set but fails to perform on an independent sample. This occurs because the model incorporates and “learns” individual characteristics of each sample in the training set. Leave-one-out cross-validation was used to verify that the prediction models generated above would work on independent samples that the models had not encountered previously. In this cross-validation method, a single sample is removed from the training set, and the predictor is developed again using the remaining data. The resulting model is then used to predict the sample that was removed. This method is repeated with each individual sample taken out. Since no sample is predicted from a model that includes that sample, this method provides an unbiased estimate of predictor accuracy.

When the predictors developed above were evaluated by leave-one-out cross-validation, all but one of the 21 MCL samples were correctly identified as MCL and none of the 489 non-MCL samples were mistakenly identified as MCL.

Example 12 Identification of Lymphoma Samples as MCL Based on Bayesian Gene Expression Data from a Lymphochip Microarray

Lymphoma samples with morphology consistent with MCL were identified by pathological review. Since t(11; 14) translocation and cyclin D1 overexpression have been consistently associated with MCL, cyclin D1 mRNA levels were measured in each sample by quantitative RT-PCR. Of the 101 samples analyzed, 92 expressed cyclin D1 mRNA. These 92 samples, which were deemed the “core group” of MCLs, were divided into a training set and a validation set. Gene expression was measured in all 101 samples using a Lymphochip microarray (Alizadeh 1999). For comparison, gene expression was measure in 20 samples identified as SLL. In addition, MCL expression data was compared to expression data obtained previously for GCB (134 cases) and ABC (83 cases) (Rosenwald 2002). Several thousand genes were differentially expressed between cyclin D1-positive MCL and the other lymphoma types with high statistical significance (p<0.001). A complete listing of these genes is available at Rosenwald et al., Cancer Cell, 3:185-197 (2003), which is referenced therein at page 194 and which is hosted by the Lymphoma/Leukemia Molecular Profiling Project Gateways at the National Institute of Health web site.

Three different binary predictor models were developed: MCL vs. SLL, MCL vs. GCB, and MCL vs. ABC. Each of these models was designed to calculate the probability that a sample was MCL rather than the other lymphoma type in the pair. For each pair, the genes that were most differentially expressed between MCL and the other lymphoma type in the pair were identified, and the difference in expression between the lymphoma types was quantified using a Student's t-test. An LPS was then calculated for each sample using the following formula:

${{{LPS}(X)} = {\sum\limits_{j \in G}{t_{j}X_{j}}}},$ where X_(j) is the expression of gene j in sample X and t_(j) is the t-statistic for the difference in expression of gene j between the two lymphoma types in the pair. Cyclin D1 was excluded from the calculation of LPS so that the model could be used to identify potential MCL cases that were cyclin D1 negative.

After an LPS had been formulated for each lymphoma sample, the mean and standard deviation of these LPS's was calculated for each lymphoma type. For a new sample X, Bayes' rule can be used to estimate the probability q that the sample belongs to MCL rather than the second lymphoma type in the pair using the following equation:

${q\left( {X\mspace{14mu}{is}\mspace{14mu}{MCL}} \right)} = \frac{\phi\left( {{{{LPS}(X)};{\hat{\mu}}_{MCL}},{\hat{\sigma}}_{MCL}} \right)}{{\phi\left( {{{{LPS}(X)};{\hat{\mu}}_{MCL}},{\hat{\sigma}}_{MCL}} \right)} + {\phi\left( {{{{LPS}(X)};{\hat{\mu}}_{2}},{\hat{\sigma}}_{2}} \right)}}$ where φ(x; μ, σ) is the normal density function with mean μ and standard deviation σ, {circumflex over (μ)}_(MCL) and {circumflex over (σ)}_(MCL) are the sample mean and variance of the LPS values for MCL, and {circumflex over (μ)}₂ and {circumflex over (σ)}₂ are the sample mean and variance of the LPS values for the second lymphoma type of the pair. A cut-off point of 90% was selected for assigning a sample to a particular lymphoma type. Every sample in the training set were classified correctly using this model (FIG. 16). When applied to the validation set, the model correctly classified 98% of the cyclin D1-positive MCL cases as MCL (FIG. 16).

This diagnostic test was applied to nine lymphoma cases that were morphologically consistent with MCL, but negative for cyclin D1 expression. Seven of these samples were classified as MCL, one was classified as GCB, and one was not assigned to any lymphoma type because none of the pairs generated a probability of 90% or greater.

Example 13 Classification of DLBCL Samples Based on Bayesian Analysis of Gene Expression Data from the Lymphochip Microarray

A statistical method to classify DLBCL samples based on Bayesian analysis was developed using gene expression data obtained using the Lymphochip cDNA microarray (Rosenwald 2002). This data is available at http://llmpp.nih.gov/DLBCL. The data was divided into two sets: a training set used to create and optimize the prediction model, and a validation set to evaluate the performance of the model. The training set consisted of 42 ABC DLBCL samples and 67 GCB DLBCL samples, while the validation set consisted of 41 ABC DLBCL samples, 67 GCB DLBCL samples, and 57 type 3 DLBCL samples (Shipp 2002).

Genes that were listed as present on >50% of the samples were identified, and the signal value for these genes on each microarray was normalized to 1,000. After normalization, all signal values under 50 were set to 50. A log₂ transformation was then performed on all the signal values.

An LPS for distinguishing between two lymphoma types was calculated for each sample X in the training set using an equation:

${{{LPS}(X)} = {\sum\limits_{j}{t_{j}X_{j}}}},$ where X_(j) represents the expression level of gene j and t_(j) is a scaling factor whose value depends on the difference in expression of gene j between the two lymphoma types. The scaling factor used in this example was the t-statistic generated by a t test of the difference in gene j expression between two lymphoma types. Only those genes with the largest t-statistics were included when calculating the LPS for each sample. The list of genes used to generate the LPS was narrowed further by including only those genes that were most variably expressed within the training set. Only genes in the top third with respect to variance were included. Genes that displayed a correlation with proliferation or lymph node signatures (Shaffer 2001; Rosenwald 2002) were eliminated from consideration, because these genes are often variably expressed within samples from a single lymphoma type (Rosenwald 2002).

Since the LPS is a linear combination of gene expression values, its distribution within each lymphoma type should be approximately normal, provided that it includes a sufficient number of genes and the correlation structure of those genes is not extreme. The mean and variance of these normal distributions within a lymphoma type can then be estimated from the combined LPS's of all samples within the type. The LPS distribution of two lymphoma types can be used to estimate the probability that a new sample belongs to one of the types using Bayes' rule. The probability q that a sample Y belongs to lymphoma type 1 can be determined by an equation:

${q\left( {Y\mspace{14mu}{is}\mspace{14mu}{subtype}\mspace{14mu} 1} \right)} = \frac{\phi\left( {{{{LPS}(Y)};{\hat{\mu}}_{1}},{\hat{\sigma}}_{1}} \right)}{{\phi\left( {{{{LPS}(Y)};{\hat{\mu}}_{1}},{\hat{\sigma}}_{1}} \right)} + {\phi\left( {{{{LPS}(Y)};{\hat{\mu}}_{2}},{\hat{\sigma}}_{2}} \right)}}$ where φ(x; μ, σ) is the normal density function with means μ and standard deviation φ, {circumflex over (μ)}₁ and {circumflex over (σ)}₁ are the sample mean and variance of the LPS values for lymphoma type 1, and {circumflex over (μ)}₂ and {circumflex over (σ)}₂ are the sample mean and variance of the LPS values for lymphoma type 2. This calculation was used to determine the probability that each sample in the training set belonged to GCB or ABC. A sample was classified as a particular type if it had a 90% or greater probability of belonging to that type. The number of genes in the predictor model was optimized based on the accuracy with which the predictor classified samples into the ABC or GCB subtypes defined previously by hierarchical clustering (Rosenwald 2002). The final predictor incorporated 27 genes, and correctly classified 87% of the training set samples into the subtype to which they had been assigned by hierarchical clustering (FIG. 17). The genes included in the predictor are listed in Table 2391.

TABLE 2391 Gene GENBANK Affymetrix probe UNIQID Unigene ID Build 167 symbol gi Accession set ID 19375 235860 FOXP1 60498986 NM_001012505.1 224837_at 19346 109150 SH3BP5 109134343 NM_001018009.2 201810_s_at 19227 193857 LOC96597 89041248 XM_378655.2 233483_at 16049 439852 IGHM 21757751 AK097859.1 32529 55098 CCDC50 33186926 NM_174908.2 226713_at 24729 127686 IRF4 4505286 NM_002460.1 204562_at 24899 81170 PIM1 31543400 NM_002648.2 209193_at 19348 NA IGHM 21757751 AK097859.1 27565 444105 ENTPD1 147905699 NM_001098175.1 209474_s_at 17227 170359 IL16 148833502 NM_004513.4 209827_s_at 26919 118722 FUT8 30410721 NM_004480.3 203988_s_at 24321 171262 ETV6 153267458 NM_001987.4 217377_x_at 29385 167746 BLNK 40353774 NM_013314.2 207655_s_at 16858 376071 CCND2 16950656 NM_001759.2 200951_s_at 31801 386140 BMF 51558687 NM_001003940.1 226530_at 19234 418004 PTPN1 18104977 NM_002827.2 202716_at 26385 307734 MME 116256328 NM_000902.3 203434_s_at 24361 388737 76779240 BC106050.1 24570 446198 SAMD12 156119596 NM_001101676.1 24904 18166 KIAA0870 50345869 NM_014957.2 212975_at 24429 155024 BCL6 21040323 NM_001706.2 228758_at 28224 387222 NEK6 34147501 NM_014397.3 223158_s_at 27673 124922 LRMP 42789728 NM_006152.2 204674_at 24376 317970 SERPINA11 110225348 NM_001042518.1 17496 300592 MYBL1 122937230 NM_001080416.1 213906_at 17218 283063 LMO2 6633806 NM_005574.2 204249_s_at 28338 78877 ITPKB 38569399 NM_002221.2 203723_at

Since the samples used to estimate the distribution of the LPS's were the same samples used to generate the model, there was a possibility of overfitting. Overfitting would result in a model that indicates a larger separation between the LPS's of two lymphoma types than would be found in independent data. To ensure that overfitting was not taking place, the model was tested on the validation set. The reproducibility of the predictor model was verified by its ability to correctly classify 88% of the samples in the validation set (FIG. 18). Interestingly, 56% of the DLBCL samples that had been placed in the type 3 subtype by hierarchical clustering were classified as either ABC or GCB using this Bayesian model.

In previous experiments, the genes that were used to distinguish GCB and ABC were deliberately selected to include those that were preferentially expressed in normal GC B cells (Alizadeh 2000; Rosenwald 2002). In the present analysis, the predictor model was not biased a priori to include such genes. The ABC and GCB lymphoma types as defined by the Bayesian model were analyzed for differential expression of GC B cell restricted genes. Thirty seven genes were found to be both more highly expressed in GC B cells than at other stages of differentiation (p<0.001) and differentially expressed between DLBCL subtypes (p<0.001) (FIG. 19A). These 37 genes are listed in Table 2392.

TABLE 2392 UNIQID Unigene ID Build 167 Gene symbol 28014 300592 MYBL1 24376 317970 SERPINA11 24429 155024 BCL6 16886 124922 LRMP 27374 283063 LMO2 29912 446198 24510 266175 PAG 24854 439767 TOX 32171 307734 MME 24361 388737 19365 171857 Cyorf15a 27292 272251 KLHL5 24822 283794 PCDHGC3 30923 446195 24825 88556 HDAC1 31696 91139 SLC1A1 26976 434281 PTK2 19279 49614 GCET2 17866 1765 LCK 24386 437459 MYO1E 33013 293130 VNN2 25126 30498 157441 SPI1 26512 379414 MFHAS1 26582 153260 SH3KBP1 17840 132311 MAP2K1 26000 25155 NET1 24323 149342 AICDA 30922 435904 C21orf107 30641 79299 LHFPL2 19308 179608 DHRS9 24455 405387 30034 300208 SEC23IP 24977 169939 HS2ST1 24449 206097 RRAS2 30763 446198 27987 73792 CR2 All but two (AICDA and DHRS9) of these 37 genes were more highly expressed in GCB than in ABC. This demonstrates that the DLBCL subtypes defined by the Bayesian predictor seem to differ with respect to their cell of origin, with GCB retaining the gene expression program of normal GC B cells.

ABC, on the other hand, displayed higher expression of genes characteristic of plasma cells (FIG. 19B). Twenty four genes were found to be both more highly expressed in plasma cells than in B cells at earlier developmental stages (p<0.001) and differentially expressed between the DLBCL subtypes (p<0.001). These 24 genes are listed in Table 2393.

TABLE 2393 UNIQID Unigene ID Build 167 Gene symbol 16614 127686 IRF4 26907 118722 FUT8 31104 313544 NS 19219 355724 CFLAR 26174 28707 SSR3 24566 169948 KCNA3 34500 442808 B4GALT2 26991 314828 UPP1 30191 438695 FKBP11 27402 259855 EEF2K 26096 434937 PPIB 15887 2128 DUSP5 32440 512686 C20orf59 34827 429975 PM5 29232 437638 XBP1 17763 76640 RGC32 32163 445862 RAB30 17814 5353 CASP10 31460 409223 SSR4 26693 83919 GCS1 25130 409563 PACAP 16436 267819 PPP1R2 31610 76901 PDIR 28961 212296 ITGA6 The majority of these plasma cell-restricted genes were more highly expressed in ABC than in GCB. Eight of the 32 genes encode proteins that reside and function in the endoplasmic reticulum (ER) or Golgi apparatus, suggesting that ABCs have increased the intracellular machinery for protein secretion. These eight genes are denoted in the above list by the designation “ER” or “golgi” in parentheses. Another gene on this list, XBP-1 transcription factor, encodes a protein that is required for plasma cell differentiation (Reimold 2001) and is involved in the response to unfolded proteins in the ER (Calfon 2002). ABC have not undergone full plasmacytic differentiation, however, because other key plasma cell genes such as Blimp-1 were not more highly expressed in ABC.

Example 14 Classification of DLBCL Samples Based on Bayesian Analysis of Gene Expression Data from the Affymetrix HU6800 Microarray

The prediction method described in Example 13 above was applied to gene expression data from 58 DLBCL samples obtained using an Affymetrix HU 6800 oligonucleotide microarray (Shipp 2002). This data is available at www.genome.wi.mit.edu/MPR/lymphoma. The first step in analyzing this data was to exclude all microarray features with a median signal value of <200 across the samples. Multiple microarray features representing the same gene were then averaged. Of the 27 genes in the DLBCL subtype predictor developed using the Lymphochip data (above), only 14 were represented on the Affymetrix array and passed this filtering process. These 14 genes are listed in Table 2394.

TABLE 2394 Gene GENBANK Affymetrix probe UNIQID Unigene ID Build 167 symbol gi Accession set ID 24729 127686 IRF4 4505286 NM_002460.1 204562_at 17227 170359 IL16 148833502 NM_004513.4 209827_s_at 26907 118722 FUT8 30410721 NM_004480.3 203988_s_at 27565 444105 ENTPD1 147905699 NM_001098175.1 209474_s_at 16858 376071 CCND2 16950656 NM_001759.2 200951_s_at 24899 81170 PIM1 31543400 NM_002648.2 209193_at 16947 418004 PTPN1 18104977 NM_002827.2 202716_at 16049 439852 IGHM 21757751 AK097859.1 26385 307734 MME 116256328 NM_000902.3 203434_s_at 27673 124922 LRMP 42789728 NM_006152.2 204674_at 24429 155024 BCL6 21040323 NM_001706.2 228758_at 17218 283063 LMO2 6633806 NM_005574.2 204249_s_at 28338 78877 ITPKB 38569399 NM_002221.2 203723_at 17496 300592 MYBL1 122937230 NM_001080416.1 213906at These 14 genes were used to create a new DLBCL subtype predictor in which the LPS scaling coefficients were again calculated based on the DLBCL subtype distinction in the Lymphochip data set (Rosenwald 2002). To account for systematic measuring differences between the Affymetrix and Lymphochip microarrays, the expression value of each gene on the Affymetrix microarray was shifted and scaled to match the mean and variance of the corresponding expression values on the Lymphochip. The adjusted expression values for each of the 14 genes were then used to calculate LPS's for each sample. DLBCL subtype membership was again assigned on a cut-off of 90% certainty. Several observations suggested that the predictor identified ABC and GCB samples within the Affymetrix data set that were comparable to those found in the Lymphochip data set. First, the relative proportions of ABC (29%) and GCB (53%) were very similar to the corresponding proportions in the Lymphochip data set (34% and 49%, respectively). Second, 43 genes were found to be differentially expressed between the two DLBCL subtypes with high significance (p<0.001) in the Affymetrix data. This number is substantially higher than would be expected by chance, given that the Affymetrix microarray measures the expression of approximately 5,720 genes. The symbols for these 43 genes were: IGHM; TCF4; IRF4; CCND2; SLA; BATF; KIAA0171; PRKCB1; P2RX5; GOT2; SPIB; CSNK1E; PIM2; MARCKS; PIM1; TPM2; FUT8; CXCR4; SP140; BCL2; PTPN1; KIAA0084; HLA-DMB; ACP1; HLA-DQA1; RTVP1; VCL; RPL21; ITPKB; SLAM; KRT8; DCK; PLEK; SCA1; PSIP2; FAM3C; GPR18; HMG14; CSTB; SPINK2; LRMP; MYBL1; and LMO2. Third, the 43 genes differentially expressed between the types included 22 genes that were not used in the predictor but were represented on Lymphochip arrays. Fourteen of these 22 genes were differentially expressed on the Lymphochip array with high statistical significance (p<0.001). Finally, the expression of the c-rel gene was previously found to correspond to amplification of the c-rel genomic locus in DLBCL tumor cells, and oncogenic event occurring in GCB but not ABC (Rosenwald 2002). In the Affymetrix data set, c-rel was differentially expressed between the two subtypes (p=0.0025), and was highly expressed only in a subset of GCB's.

Example 15 Identification of DLBCL Samples as PMBL Based on Bayesian Analysis of Gene Expression Data from the Lymphochip Microarray

310 lymphoma biopsy samples identified as DLBCL by a panel of hematopathologists were divided into a 36 sample training set and a 274 sample validation set, with the validation set consisting of the DLBCL samples as classified previously in Example 13. All patients from whom the samples were derived had been treated with anthracycline-containing multiagent chemotherapy protocols, with some patients additionally receiving radiation therapy. The training set was profiled for gene expression using Lymphochip microarrays comprising 15, 133 cDNA elements as described previously (Alizadeh 2000). The data is available at the web site companion for Rosenwald et al., J. Exp. Med., 198: 851-862 (2003), which is referenced therein at page 852 and which is hosted by Lymphoma/Leukemia Molecular Profiling Project Gateway at the National Institute of Health web site. The validation set had previously been profiled using Lymphochip microarrays comprising 12,196 cDNA elements (Rosenwald 2002). This data is available at the web site companion for Rosenwald et al., New Eng. J. Med., 346: 1937-1947 (2002), which is referenced therein at page 1938 and which is hosted by the Lymphoma/Leukemia Molecular Profiling Project Gateway at the National Institute of Health web site.

A hierarchical clustering algorithm (Eisen 1998) was used to organize the genes by their expression patterns across the 36 samples in the training set. A large group of genes that were more highly expressed in lymphomas with mediastinal involvement than in other DLBCLs was shown to be tightly clustered in the resulting dendrogram (FIG. 20A). This cluster of genes included two genes, MAL and FIG. 1, previously shown to be highly expressed in PMBL (Copie-Bergman 2002; Copie-Bergman 2003). Several of the lymphomas with mediastinal involvement did not express this set of putative PMBL signature genes, and it was suspected that these samples were more likely to be conventional DLBCL than PMBL. Hierarchical clustering was used to organize the samples according to their expression of the PMBL signature genes, resulting in two major clusters of cases (FIG. 20B). One cluster contained 21 samples designated “PMBL core” samples by virtue of their higher expression of PMBL signature genes. The other cluster contained some samples that had virtually no expression of these genes, and other samples that did express these genes but at lower levels than the PMBL core samples.

A gene expression-based method for distinguishing PMBL core cases from GCB and ABC DLBCL cases based on Bayesian analysis was developed using the methods described in Examples 13 and 14. A set of genes were selected that were differentially expressed between the PMBL core samples and both GCB and ABC (p<0.001). This set of genes included all of the PMBL signature genes identified by hierarchical clustering (FIG. 20A), as well as a large number of additional genes. Many of the genes in this set belonged to the lymph node gene expression signature (Alizadeh 2000; Rosenwald 2002). These genes were excluded from the final predictor because they might cause some DLBCL samples with higher expression of lymph node gene expression signature genes to be misclassified as PMBL. The list of PMBL distinction genes was refined by adding a requirement that they also be differentially expressed between the PMBL core samples and a subgroup of six DLBCL samples with higher expression of lymph node gene expression signature genes (p<0.001). The resulting set of 46 genes included 35 genes that were more highly expressed in PMBL and 11 genes that were more highly expressed in DLBCL (FIG. 21A). The 46 genes in this set were PDL2, SNFT, IL13RA1, FGFR1, FLJ10420, CCL17/TARC, TNFRSF8/CD30, E2F2, MAL, TNFSF4/OX40 ligand, IL411/Fig1, IMAGE:686580, BST2, FLJ31131, FCER2/CD23, SAMSN1, JAK2, FLJ0066, MST1R, TRAF1, SLAM, LY75, TNFRSF6/Fas, FNBP1, TLR7, TNFRSF17/BCMA, CDKN1A/p21CIP1, RGS9, IMAGE:1340506, NFKB2, KIAA0339, ITGAM, IL23A, SPINT2, MEF2A, PFDN5, ZNF141, IMAGE:4154313, IMAGE:825382, DLEU1, ITGAE, SH3BP5, BANK, TCL1A, PRKAR1B, and CARD 11. A series of linear predictor scores were generated based on the expression of this gene set. Based on the distribution of linear predictor scores within a particular lymphoma type, Bayes' rule can be used to estimate the probability that a particular sample belongs to either of the two types. An arbitrary probability cut-off of 90% or greater was used to classify a sample as a particular lymphoma type. All of the PMBL core samples were classified as PMBL using this method, as were six of the other lymphoma samples with mediastinal involvement. However, nine of the lymphoma samples with mediastinal involvement were classified as a DLBCL, as were all of the GCB and ABC samples.

In the validation set, 11 samples were identified on clinical grounds as being consistent with a diagnosis of PMBL, and the Bayesian model classified nine of these as PMBL (FIG. 21B). Interestingly, 12 of the remaining 263 DLBCL samples were classified as PMBL by the predictor. FIG. 21B shows that these cases were indistinguishable by gene expression from the nine cases diagnosed as PMBL on clinical grounds. As expected, the average expression of the PMBL predictor genes in the 249 samples classified as DLBCL was notably lower than in the 22 PMBL cases. Thus, PMBL represents a third subgroup of DLBCL than can be distinguished from ABC and GCB by gene expression profiling.

Table 2395 compares the clinical parameters of patients assigned to the PMBL, ABC, and GCB subgroups of DLBCL using this prediction method.

TABLE 2395 ABC GCB PMBL PMBL PMBL DLBCL DLBCL Training set Validation set All cases P value Median age 66 61 33 33 33 4.4E−16 Age <35  5% 10% 52% 56% 53% 7.2E−14 Age 35-60 29% 38% 44% 28% 37% Age >60 66% 52%  4% 17%  9% Gender = male 59% 53% 44% 50% 47% 0.38 Female <35  2%  3% 32% 39% 35% 1.1E−12 Male <35  2%  7% 20% 17% 19% Female 35-60  6% 18% 24%  6% 16% Male 35-60 23% 19% 20% 22% 21% Female >60 33% 25%  0%  6%  2% Male >60 34% 27%  4% 11%  7% PMBL patients were significantly younger than other DLBCL patients, with a median age at diagnosis of 33 years compared with a median age of 66 and 61 years for ABC and GCB patients, respectively. Although there was no significant difference in gender distribution among the DLBCL subgroups, young women (<35 years) accounted for 35% of PMBL patients, more than any other DLBCL subgroup. Young men (<35 years) were also more frequently represented in the PMBL subgroup, accounting for 19% of the patients. Correspondingly, older men and women (age>60) were significantly underrepresented in the PMBL subgroup. These clinical characteristics were observed in both the training set and the validation set of PMBL cases, demonstrating that the PMBL predictor reproducibly identified a clinically distinct subgroup of DLBCL patients.

The PMBL subgroup defined by the PMBL predictor had a relatively favorable overall survival rate after therapy (FIG. 22). PMBL patients had a five-year survival rate of 64%, superior to the 46% rate seen in DLBCL patients as a whole (p=0.0067). The survival of the PMBL subgroup was significantly better than the 30% five-year survival rate of the ABC subgroup (FIG. 22; p=5.8E-5), but only marginally better than the 59% five-year survival rate of the GCB subgroup (p=0.18).

Example 16 Classification of Lymphomas into Types Based on Bayesian Analysis of Gene Expression Data from the Lymph Dx Microarray

Based on the clustering of the Lymph Dx microarray signals for the DLBCL samples, a cluster of “proliferation signature” genes and a cluster of “lymph node signature” genes were identified. The expression of these genes was averaged to form a proliferation signature and a lymph node signature. Each gene represented on the Lymph Dx microarray was placed into one of three “gene-list categories” based on its correlation with the proliferation or lymph node gene signatures. “Proliferation” genes were defined as those genes for which the correlation between their expression and the proliferation signature was greater than 0.35. Lymph node genes were defined as those genes for which the correlation between their expression and the lymph node signature was greater than 0.35. The remaining genes on the array were classified as standard genes. This classification resulted in 323 proliferation genes and 375 lymph node genes.

Two stages of lymphoma classification were performed using the gene expression data obtained for the above samples using the Lymph Dx microarray. The general procedure used to classify the samples is presented in flow chart form in FIG. 1.

For the first stage of expression analysis, the samples were divided into five types: FL, MCL, SLL, FH, and a class of aggressive lymphomas that included DLBCL and BL. Samples obtained from subjects with other diagnoses (e.g., MALT, LPC) were omitted from this analysis. Data from the Lymph Dx microarray was then used to compare gene expression in each possible lymphoma type pair (e.g., FH vs. FL, MCL vs. SLL, etc.). This resulted in the creation of ten “pair-wise models” (one for each possible lymphoma type pair) for predicting whether a sample fell into a particular lymphoma type.

For each lymphoma type pair, the difference in expression between the two types for every gene on the microarray was calculated, and a t-statistic was generated to represent this difference. Within each gene-list category (proliferation, lymph node, and standard), individual genes were ordered based on the absolute value of their t-statistic. Only those genes that displayed a statistically significant difference in expression between the two types were included in the model. Those genes with largest absolute t-statistics in each gene-list category were then used to generate a linear predictor score (LPS) for each sample. For a sample X and a set of genes G, the LPS was defined as:

${{{LPS}(X)} = {\sum\limits_{j \in G}{t_{j}X_{j}}}},$ where X_(j) is the expression of gene j in the sample and t_(j) is the t-statistic representing the difference in expression of gene j between the two lymphoma types. This formulation of LPS, known as the compound covariate predictor, has previously been used successfully (Radmacher 2002; Rosenwald 2003; Wright 2003). Other ways to formulate an LPS include Fisher linear discriminant analysis (Dudoit 2002), weighted voting (Golub 1999), linear support vector machines (Ramaswamy 2001), and nearest shrunken centroids (Tibshirani 2002).

In order to optimize the number of genes used to generate the LPS, a series of LPS's were generated for each sample using between five and 100 genes from each gene-list category. The optimal number of genes is that number which generates a maximum t-statistic when comparing the LPS of two samples from different lymphoma types (FIG. 23). This optimization procedure was repeated for every gene-list category in every pair-wise model, meaning that 30 optimizations were performed in all.

It was recognized that for some pair-wise models, it would be useful to calculate LPS's using different combinations of gene-list categories. LPS's were calculated for each sample using four different combinations. In the first, LPS was calculated using the standard genes only. In the second, LPS's were calculated for both the standard and proliferation genes, but not the lymph node genes. In the third, LPS's were calculated for both the standard and lymph node genes, but not the proliferation genes. In the fourth, LPS's were calculated using all three gene-list categories.

Depending on the number of gene-list categories included, between one and three LPS's were calculated for each sample in the pair-wise models. Thus, each sample could be thought of as a vector in a space of between one and three dimensions. Since the LPS's were sums of individual expressions, it was reasonable to approximate the distributions as normal. Multivariate normal distributions are defined by two quantities: a mean vector, which indicates the average value of each of the models within a given lymphoma type, and a covariance matrix, which indicates the magnitude and orientation spread of points away from this center. Both of these quantities can be estimated empirically from the observed data. FIG. 24 shows the Standard and Proliferation LPS's for the FL vs. DLBCL/BL pair-wise model. The dotted lines indicate the standard deviations from the fitted multivariate normal distributions.

Once the multidimensional distributions have been estimated, Bayes' rule (Bayes 1763) can be used to estimate the probability that a given sample belongs to one lymphoma type or another. Bayesian analysis of an LPS has been successfully employed in the past to distinguish DLBCL subtypes (Rosenwald 2003, Wright 2003). For a sample X, the probability q of the sample belonging to a first lymphoma type rather than a second lymphoma type can be calculated using the formula:

$q = \frac{\phi\left( {{{{LPS}(X)};{\hat{\mu}}_{1}},{\hat{\sigma}}_{1}} \right)}{{\phi\left( {{{{LPS}(X)};{\hat{\mu}}_{1}},{\hat{\sigma}}_{1}} \right)} + {\phi\left( {{{{LPS}(X)};{\hat{\mu}}_{2}},{\hat{\sigma}}_{2}} \right)}}$ where LPS(X) is the linear predictor score for sample X, φ(x; μ, σ) is the normal density function with mean μ and standard deviation σ, {circumflex over (μ)}₁ and {circumflex over (σ)}₁ are the mean and variance of the LPS's for the first lymphoma type, and {circumflex over (μ)}₂ and {circumflex over (σ)}₂ are the mean and variance of the LPS's for the second lymphoma type. Using this equation, a single probability q value can be developed for each sample and for each of the four LPS combinations. This q value can then be used to classify a sample as a first lymphoma type, a second lymphoma type, or unclassified. Samples with the highest q values are classified as the first lymphoma type, while samples with the lowest q values are classified as the second lymphoma type. Samples with middle range q values are deemed unclassified. Classifying the samples in this manner requires two cut-off points: a lower cut-off point between the second lymphoma type and unclassified, and an upper cut-off point between unclassified and the first lymphoma type. To develop these cut-off points, samples were ordered by their q values, and each possible cut-off point between adjacent samples was considered. To ensure that the cut-off points were reasonable, the lower cut-off point was restricted to between 0.01 and 0.5 and the upper cut-off point was restricted to between 0.5 and 0.99.

Every cut-off point and model combination was analyzed by the following equation: 3.99*[(% of type 1 misidentified as type 2)+(% of type 2 misidentified as type 1)]+[(% of type 1 unclassified)+(% of type 2 misidentified)]. Using this equation, the cut-off point would be adjusted to allow an additional error only if this adjustment resulted in four or more unclassified samples becoming correctly classified. The final model and cut-off point for a given pair-wise analysis was that which minimized this equation. The equation utilizes percentages rather than the actual number of cases in order to account for the different number of samples in each class.

All cut-off points between a given pair of adjacent q-values will produce the same division of data. Since cut-off point optimality is defined in terms of dividing the data into subtypes, all cut-off points between a pair of borderline cases will be equally optimal. In choosing where to place the actual cut-off point values, values were chosen that would lead to a larger unclassified region. When the lower cut-off point was being defined, a value would be chosen that was ⅕ of the way from the smallest borderline case to the largest. When the upper cut-off point was being defined, a value would be chosen that was ⅘ of the way from the smallest borderline case to the largest. FIG. 25 illustrates the q-results of optimizing the cut-point for the FL versus DLBCL/BL samples. The optimal lower cut-off point for these samples was found at q=0.49, while the optimal upper cut-off point was found at q=0.84. FIG. 26 indicates how this choice of cut-off points divided the space of LPS's.

The above procedures resulted in a series of pair-wise models for comparing every lymphoma type to every other lymphoma type. If there are n types, then there will be n−1 pair-wise models for each type. Since there were five lymphoma types in the stage 1 analysis, each type was involved in 4 pair-wise models. For instance, there were four different pair-wise models for MCL: MCL vs. FH, MCL vs. FL, MCL vs. SLL, and MCL vs. DLBCL/BL. For each sample tested, each pair-wise model will produce one of three possible results: 1) the sample belongs to the first lymphoma type of the pair-wise model, 2) the sample belongs to the second lymphoma type of the pair-wise model, or 3) the sample is unclassified. If each of the n−1 models agrees that the sample belongs to a particular lymphoma type, then the sample is designated as belonging to that type. If the n−1 models do not all agree that the sample belongs to a particular lymphoma type, the sample is designated as unclassified.

To ensure that the above methods did not result in overfitting (i.e., models that fit particular idiosyncrasies of the training set but fail when applied to independent data), the models were validated by leave-one-out cross-validation fashion (Hills 1966). Each sample was removed from the data one at a time, and a predictive model was developed as described above using the remaining data. This model was then used to predict the sample that was removed. Since the model being used to predict a given sample was generated from data that did not include that sample, this method provided an unbiased estimate of the accuracy of the model.

The results of the leave-one-out predictions are set forth in Tables 2396 and 2397, below. The rows in each table correspond to different sample groups, while the columns indicate the prediction results. The standard to which the prediction results were compared in this stage was the diagnoses of a panel of eight expert hematopathologists who used histological morphology and immunohistochemistry to classify the samples. Table 2396 provides classification results for the five lymphoma types tested (DLBCL/BL, FL, FH, MCL, SLL), while Table 2397 provides more specific results for classification of subtypes within these five lymphoma types. The results set forth in Table 2396 are also summarized in FIG. 27.

TABLE 2396 DLBCL/BL FL FH MCL SLL Unclassified Total % Correct % Unclassified % Error DLBCL/BL 249 6 0 0 0 7 262 95% 2% 3% FL 5 154 0 0 0 14 173 89% 8% 3% FH 0 0 17 0 0 0 17 100% 0% 0% MCL 0 0 0 22 0 0 22 100% 0% 0% SLL 0 0 0 0 14 0 14 100% 0% 0%

TABLE 2397 DLBCL/BL FL FH MCL SLL Unclassified Total % Correct % Unclassified % Error ABC 78 0 0 0 0 0 78 100% 0% 0% GCB 77 4 0 0 0 4 85 91% 5% 5% PMBL 33 0 0 0 0 0 33 100% 0% 0% Unclassified 27 1 0 0 0 2 30 90% 7% 3% DLBCL DLBCL (not yet 14 0 0 0 0 1 15 93% 7% 0% subclassed) BL 20 1 0 0 0 0 21 95% 0% 5% FL grade 1 1 78 0 0 0 3 82 95% 4% 1% FL grade 2 2 58 0 0 0 3 63 92% 5% 3% FL grade 3A 2 18 0 0 0 8 28 64% 29% 7% Combined FL 5 154 0 0 0 14 173 89% 8% 3% grades 1, 2, 3A FL grade 3B 2 1 0 0 0 4 7 14% 57% 29% FL unknown grade 3 11 0 0 0 0 14 79% 0% 21% FH 0 0 17 0 0 0 17 100% 0% 0% MCL 0 0 0 22 0 0 22 100% 0% 0% SLL 0 0 0 0 14 0 14 100% 0% 0%

As seen in Table 2396, perfect prediction of SLL, MCL, and FH samples was obtained. The success rate for predicting FL and the aggressive lymphomas (DLBCL/BL) was also very good, with only 3% of the samples being classified incorrectly. As seen in Table 2397, perfect prediction was also obtained for ABC and PMBL samples within the DLBCL samples.

Example 17 Classification of DLBCL/BL Samples into Subtypes Based on Bayesian Analysis of Gene Expression Data from the Lymph Dx Microarray

Samples identified as DLBCL/BL in Example 16 were subdivided into four types: ABC, GCB, PMBL, and BL. These samples were then used to generate six pair-wise models using the same procedure described in Example 16. The results of the leave-one-out predictions using these pair-wise models are set forth in Table 2398, below. These results are also summarized in FIG. 28. The rows in the table correspond to different sample groups, while the columns indicate the prediction results. In this stage, the ability of the prediction method to identify BL was again measured against the diagnoses of hematopathologists. The ability of the prediction method to identify the various DLBCL subtypes, on the other hand, was measured against previous studies in which this distinction between subtypes was based on gene expression data from a Lymphochip microarray (Alizadeh 2000, Rosenwald 2002, Rosenwald 2003, Wright 2003).

TABLE 2398 ABC GCB PMBL BL Unclassified Total % Correct % Unclassified % Error ABC 76 0 0 0 2 78 97% 3% 0% GCB 1 66 2 4 4 77 86% 9% 5% PMBL 0 2 27 0 4 33 82% 12% 6% Unclassified DLBCL 5 9 1 1 11 27 NA 41% 4% DLBCL (not yet 5 5 0 1 3 14 NA 21% 7% subclassed) BL 0 1 0 18 1 20 90% 5% 5% FL grade 1 0 1 0 0 0 1 FL grade 2 0 1 0 0 1 2 FL grade 3A 0 2 0 0 0 2 Combined FL grades 1, 2, 0 4 0 0 1 5 3A FL grade 3B 0 1 0 0 1 2 FL unknown grade 0 1 0 1 1 3

As seen in Table 2398, only 1 of the 20 BL lymphoma samples was classified incorrectly. The classification of DLBCL into subtypes was also quite effective. All previously identified ABC subtype samples were again assigned to the ABC subtype, while only 5% of the GCB samples and 6% of the PMBL samples were assigned to a different subtype than they were assigned to previously.

The above classification was implemented using S+ software and the S+ subtype predictor script contained in the file entitled “Subtype_Predictor.txt,” located in the computer program listing appendix contained on CD number 22 of 22. This S+ script implements the lymphoma prediction algorithm. When this script is pasted into an S+ script window and run in a working directory containing the data set files discussed below, it will produce a text file entitled “PredictionResults.txt,” which indicates the results of the predictive algorithm. The other files in the computer program listing appendix contain the required data sets, in their required format, for carrying out the lymphoma type identification described above. The file entitled “GeneData.txt” contains the gene expression values for each sample analyzed. This file is included in the working directory when the S+ subtype predictor script is run. The file entitled “GeneID.txt” contains information about the genes in the GeneData.txt file, and is also included in the working directory when the S+ subtype predictor script is run. This file indicates the UNIQID for each gene, as well as the extent to which the gene is associated with the lymph node and proliferation signatures (“LN.cor” and “pro.cor,” respectively). The file entitled “SampleID.txt” contains information about the samples included in the “GeneData.txt” file, specifically the original classification of all the samples. This file is also included in the working directory when the S+ subtype predictor script is run. The file entitled “PredictionResults.txt” is an example of the productive output of the prediction algorithm.

After the above model was validated using leave-one-out cross-validation, the model was re-fit using all of the data to generate a final predictor that could be applied to a new set of data. Tables 2399-2414 indicate for each of the pair wise models the list of genes used, the weight given to each of those genes, the signature with which each gene was associated, the mean values and covariance matrices associated with the subtypes being compared, and the q-value cut-points of the pair-wise model.

TABLE 2399 ABC vs. BL Gene Signature Scale UNIQID Unigene ID Build 167 Probe set symbol Standard −18.87 1101149 517226 229437_at BIC Standard −17.4 1121452 227817 205681_at BCL2A1 Standard −16.42 1123163 421342 208991_at STAT3 Standard −16.2 1121629 41691 205965_at BATF Standard −15 1134095 89555 208018_s_at HCK Standard −14.75 1132636 306278 204490_s_at CD44 Standard −14.33 1119939 170087 202820_at AHR Standard −14.25 1100138 278391 228234_at TIRP Standard −14.02 1128626 501452 219424_at EB13 Standard −13.89 1132883 432453 205027_s_at MAP3K8 Standard −13.88 1134991 444105 209474_s_at ENTPD1 Standard −13.37 1109913 355724 239629_at CFLAR Standard −13.25 1120389 75367 203761_at SLA Standard −12.99 1131497 114931 202295_s_at CTSH Standard −12.71 1115071 390476 223218_s_at MAIL Standard −12.46 1136329 132739 211675_s_at HIC Standard −12.41 1128195 115325 218699_at RAB7L1 Standard −12.37 1124381 440808 212288_at FNBP1 Standard −12.30 1100562 26608 228737_at C20orf100 Standard −12.24 1101272 179089 229584_at DKFZp434 Standard −12.18 1128536 21126 219279_at DOCK10 Standard −11.64 1098271 300670 226056_at CDGAP Standard −11.41 1119566 433506 201954_at ARPC1B Standard −11.11 1120651 80205 204269_at PIM2 Standard −10.89 1098952 62264 226841_at KIAA0937 Standard −10.80 1099939 488173 227983_at MGC7036 Standard −10.67 1134270 352119 208284_x_at GGT1 Standard −10.44 1134145 4750 208091_s_at DKFZP564 Standard −10.39 1123437 73090 209636_at NFKB2 Standard −10.17 1119884 418004 202716_at PTPM1 Standard −10.14 1129269 62919 220358_at SNFT Standard −10.13 1126293 504816 215346_at TNFRSF5 Standard −10.12 1112344 163242 242406_at Standard −10.10 1135550 221811 210550_s_at RASGRF1 Standard −10.08 1135165 170359 209827_s_at IL16 Standard −10.05 1120808 127686 204562_at IRF4 Standard −10.01 1122087 72927 206693_at IL7 Standard −9.97 1132004 415117 203217_s_at SIAT9 Standard −9.88 1114824 193370 222762_x_at LIMD1 Standard −9.87 1132034 410455 203271_s_at UNC119 Standard −9.87 1099680 210387 227677_at JAK3 Standard −9.86 1132830 31210 204908_s_at BCL3 Standard −9.79 1099631 367639 227624_at FLJ20032 Standard −9.78 1120267 256278 203508_at TNFRSF1B Standard −9.77 1124187 378738 211986_at MGC5395 Standard −9.73 1108970 140489 238604_at Standard −9.71 1136216 512152 211528_x_at HLA-G Standard −9.71 1120993 327 204912_at IL10RA Standard −9.68 1100847 97411 229070_at C6orf105 Standard −9.64 1123413 418291 209575_at IL10RB Standard −9.62 1115704 350268 224569_s_at IRF2BP2 Standard −9.58 1108237 126232 237753_at Standard −9.55 1121695 511759 206082_at HCP5 Standard −9.48 1101905 170843 230345_at Standard −9.42 1119243 440165 201171_at ATP6V0E Standard −9.39 1140457 210546 221658_s_at IL21R Standard −9.32 1098506 193400 226333_at IL6R Standard −9.31 1139805 414362 220230_s_at CYB5R2 Standard −9.30 1139037 173380 218223_s_at CKIP-1 Standard −9.28 1130533 76507 200706_s_at LITAF Standard −9.15 1098678 386140 226530_at BMF Standard −9.04 1133210 434374 205842_s_at JAK2 Standard 9.05 1116432 409362 229356_x_at KIAA1259 Standard 9.17 1097281 7037 224892_at PLDN Standard 9.17 1140018 438482 220917_s_at PWDMP Standard 9.30 1119997 367811 202951_at STK38 Standard 9.41 1119817 409194 202561_at TNKS Standard 9.55 1139842 133523 220367_s_at SAP130 Standard 9.64 1132122 307734 203434_s_at MME Standard 9.77 1119258 88556 201209_at HDAC1 Standard 9.80 1128248 234149 218802_at FLJ20647 Standard 10.38 1101211 287659 229513_at STRBP Standard 10.52 1123419 170195 209590_at BMP7 Standard 10.71 1133755 404501 207318_s_at CDC2L5 Standard 10.80 1128192 102506 218696_at EIF2AK3 Standard 10.85 1124786 22370 212847_at NEXN Standard 10.92 1130114 445084 221965_at MPHOSPH9 Standard 11.00 1126081 309763 215030_at GRSF1 Standard 11.17 1118736 96731 38340_at HIP1R Standard 11.26 1124613 296720 212599_at AUTS2 Standard 11.43 1125456 300592 213906_at MYBL1 Standard 11.60 1097177 9691 224761_at GNA13 Standard 12.11 1120400 152207 203787_at SSBP2 Standard 12.12 1139266 76640 218723_s_at RGC32 Standard 12.22 1100770 65578 228976_at Standard 12.73 1131246 153752 201853_s_at CDC25B Standard 13.48 1096503 21379 223522_at C9orf45 Standard 14.50 1124920 6150 213039_at ARHGEF1 Standard 15.03 1128360 445043 218988_at SLC35E3 Standard 15.24 1099444 434489 227407_at FLJ90013 Standard 21.03 1134582 78202 208794_s_at SMARCA4 Standard Mean ABC −4179.76 Cut 1 0.20 Mean BL −1894.68 Cut 2 0.80 Covariance ABC 53707.58 Covariance BL 194887.5

TABLE 2400 ABC vs. GCB Signature Scale UNIQID Unigene ID Build 167 Probe set Gene symbol Standard −15.31 1122645 158341 207641_at TNFRSF13B Standard −14.56 1120651 80205 204269_at PIM2 Standard −14.18 1120808 127686 204562_at IRF4 Standard −13.84 1114824 193370 222762_x_at LIMD1 Standard −13.44 1136687 59943 212345_s_at CREB3L2 Standard −13.12 1139805 414362 220230_s_at CYB5R2 Standard −12.23 1104552 193857 233483_at LOC96597 Standard −12.19 1097236 235860 224837_at FOXP1 Standard −12.06 1121629 41691 205965_at BATF Standard −11.93 1128195 115325 218699_at RAB7L1 Standard −11.72 1111503 502910 241383_at KBRAS2 Standard −11.66 1134991 444105 209474_s_at ENTPD1 Standard −11.27 1098678 386140 226530_at BMF Standard −10.9 1131074 76894 201572_x_at DCTD Standard −10.82 1135165 170359 209827_s_at IL16 Standard −10.7 1132396 118722 203988_s_at FUT8 Standard −10.54 1131541 310230 202369_s_at TRAM2 Standard −10.47 1105759 171262 235056_at ETV6 Standard −10.38 1121564 437783 205865_at ARID3A Standard −10.16 1130472 192374 200599_s_at TRA1 Standard −10.04 1132058 161999 203313_s_at TGIF Standard −10.03 1105684 195155 234973_at SLC38A5 Standard −9.95 1097735 26765 225436_at LOC58489 Standard −9.94 1115071 390476 223218_s_at MAIL Standard −9.85 1101149 517226 229437_at BIC Standard −9.83 1119884 418004 202716_at PTPN1 Standard −9.71 1134095 89555 208018_s_at HCK Standard −9.68 1135550 221811 210550_s_at RASGRF1 Standard −9.61 1098927 356216 226811_at FLJ20202 Standard −9.6 1120389 75367 203761_at SLA Standard −9.58 1133910 167746 207655_s_at BLNK Standard 9.56 1118736 96731 38340_at HIP1R Standard 9.58 1128860 323634 219753_at STAG3 Standard 9.68 1134582 78202 208794_s_at SMARCA4 Standard 9.7 1121853 98243 206310_at SPINK2 Standard 10.14 1119258 88556 201209_at HDAC1 Standard 10.19 1132122 307734 203434_s_at MME Standard 10.23 1120400 152207 203787_at SSBP2 Standard 10.48 1529344 317970 Lymph_Dx_065_at SERPINA11 Standard 10.64 1124613 296720 212599_at AUTS2 Standard 10.72 1132159 147868 203521_s_at ZNF318 Standard 10.98 1097901 266175 225626_at PAG Standard 11.1 1128287 300063 218862_at ASB13 Standard 12.26 1099686 117721 227684_at Standard 12.45 1112674 310320 242794_at MAML3 Standard 13.15 1120370 78877 203723_at ITPKB Standard 14.23 1125456 300592 213906_at MYBL1 Lymph Node 6.8 1097202 386779 224796_at DDEF1 Lymph Node 6.85 1131755 241257 202729_s_at LTBP1 Lymph Node 7.27 1136273 13775 211597_s_at HOP Lymph Node 7.35 1119424 75485 201599_at OAT Lymph Node 7.86 1095985 83883 222450_at TMEPAI Lymph Node 8.02 1124875 18166 212975_at KIAA0870 Lymph Node 8.32 1124655 79299 212658_at LHFPL2 Lymph Node 8.62 1115034 387222 223158_s_at NEK6 Proliferation −9.11 1120583 153768 204133_at RNU3IP2 Proliferation −7.87 1135492 408615 210448_s_at P2RX5 Proliferation −7.68 1127756 313544 217850_at NS Proliferation −7.57 1097195 149931 224785_at MGC29814 Proliferation −7.31 1127813 14317 217962_at NOLA3 Proliferation −7.24 1138944 84753 218051_s_at FLJ12442 Proliferation −6.99 1139226 266514 218633_x_at FLJ11342 Proliferation −6.7 1137486 441069 214442_s_at MIZ1 Proliferation −6.51 1133786 153591 207396_s_at ALG3 Proliferation −6.45 1131150 75514 201695_s_at NP Proliferation −6.45 1119076 268849 200681_at GLO1 Proliferation −6.38 1115679 8345 224523_s_at MGC4308 Proliferation −6.34 1110223 212709 239973_at Proliferation −6.3 1529338 284275 Lymph_Dx_058_s_at PAK2 Proliferation −6.24 1135164 458360 209825_s_at UMPK Proliferation −6.24 1128738 335550 219581_at MGC2776 Proliferation −6.01 1099088 14355 226996_at Proliferation −5.98 1123192 315177 209100_at IFRD2 Proliferation −5.83 1116073 146161 227103_s_at MGC2408 Proliferation 5.79 1097388 278839 225024_at C20orf77 Proliferation 6.13 1124563 249441 212533_at WEE1 Standard Lymph Node Proliferation Mean ABC −2226.57 476.67 −1096.34 Cut 1 0.50 Mean GCB −1352.02 547.18 −1005.72 Cut 2 0.74 Covariance ABC 33472.10 3418.91 4347.99 3418.91 1296.05 846.32 4347.99 846.32 1609.13 Covariance GCB 53751.59 466.34 751.08 466.34 777.74 249.29 751.08 249.29 1708.67

TABLE 2401 ABC vs. PMBL Signature Scale UNIQID Unigene ID Build 167 Probe set Gene Symbol Standard −14.61 1097236 235860 224837_at FOXP1 Standard −14.47 1104552 193857 233483_at LOC96597 Standard −13.62 1122645 158341 207641_at TNFRSF13B Standard −12.05 1135102 349845 209685_s_at PRKCB1 Standard −11.65 1096499 293867 223514_at CARD11 Standard −11.26 1124770 153261 212827_at IGHM Standard −11.25 1125010 43728 213170_at GPX7 Standard −11.13 1109545 63187 239231_at Standard −10.99 1109220 445977 238880_at GTF3A Standard −10.87 1131074 76894 201572_x_at DCTD Standard −10.68 1134517 75807 208690_s_at PDLIM1 Standard −10.63 1098604 32793 226444_at SLC39A10 Standard −10.56 1131219 109150 201810_s_at SH3BP5 Standard −10.52 1120651 80205 204269_at PIM2 Standard −10.39 1133910 167746 207655_s_at BLNK Standard −10.32 1099396 435949 227346_at ZNFN1A1 Standard −10.25 1529297 132335 Lymph_Dx_015_at Standard −10.17 1107575 424589 237033_at MGC52498 Standard −10.11 1117211 356509 233955_x_at HSPC195 Standard 10.06 1129517 −33 220712_at Standard 10.29 1139950 437385 220731_s_at FLJ10420 Standard 10.35 1097553 197071 225214_at PSMB7 Standard 10.41 1119516 6061 201834_at PRKAB1 Standard 10.47 1122772 66742 207900_at CCL17 Standard 10.55 1132762 80395 204777_s_at MAL Standard 10.77 1099265 375762 227193_at Standard 10.81 1095996 288801 222482_at SSBP3 Standard 11.14 1100770 65578 228976_at Standard 11.19 1133801 181097 207426_s_at TNFSF4 Standard 11.61 1099154 97927 227066_at MOBKL2C Standard 11.63 1120370 78877 203723_at ITPKB Standard 11.8 1112674 310320 242794_at MAML3 Standard 12.57 1105178 283961 234284_at GNG8 Standard 12.63 1124613 296720 212599_at AUTS2 Standard 13.28 1106415 169071 235774_at Standard 13.3 1121762 32970 206181_at SLAMF1 Standard 13.6 1121853 98243 206310_at SPINK2 Lymph Node 10.91 1105838 129837 235142_at ZBTB8 Lymph Node 10.99 1136273 13775 211597_s_at HOP Lymph Node 11.02 1099418 172792 227370_at KIAA1946 Lymph Node 11.46 1124875 18166 212975_at KIAA0870 Lymph Node 11.99 1120299 79334 203574_at NFIL3 Lymph Node 12.49 1135871 104717 211031_s_at CYLN2 Lymph Node 13.33 1121767 458324 206187_at PTGIR Proliferation −13.17 1138944 84753 218051_s_at FLJ12442 Proliferation −11.61 1116122 42768 227408_s_at DKFZp761O0113 Proliferation −11.16 1110223 212709 239973_at Proliferation −9.93 1120717 444159 204394_at SLC43A1 Proliferation −9.54 1110099 116665 239835_at TA-KRP Proliferation −9.49 1130942 445977 201338_x_at GTF3A Proliferation −9.28 1123192 315177 209100_at IFRD2 Proliferation −9.14 1135492 408615 210448_s_at P2RX5 Proliferation −9.03 1120011 3068 202983_at SMARCA3 Proliferation −9.01 1096738 87968 223903_at TLR9 Proliferation −8.91 1108961 292088 238593_at FLJ22531 Standard Lymph Node Proliferation Mean ABC −849.47 531.79 −1027.48 Cut 1 0.20 Mean PMBL 27.99 750.84 −872.43 Cut 2 0.80 Covariance ABC 14028.46 3705.84 3118.60 3705.84 2326.91 1083.37 3118.60 1083.37 1589.42 Covariance PMBL 19425.29 5109.98 2199.28 5109.98 2084.28 620.86 2199.28 620.86 1028.44

TABLE 2402 BL vs. GCB Signature Scale UNIQID Unigene ID Build 167 Probe set Gene Symbol Standard −12.78 1131246 153752 201853_s_at CDC25B Standard −11.35 1099444 434489 227407_at FLJ90013 Standard −10.4 1116432 409362 229356_x_at KIAA1259 Standard −10.3 1134582 78202 208794_s_at SMARCA4 Standard −10.01 1133998 76884 207826_s_at ID3 Standard −9.3 1126081 309763 215030_at GRSF1 Standard −9.19 1096503 21379 223522_at C9orf45 Standard −8.95 1529340 −99 Lymph_Dx_061_at Standard −8.88 1138128 390428 216199_s_at MAP3K4 Standard −8.8 1099152 351247 227064_at MGC15396 Standard −8.69 1133757 6113 207320_x_at STAU Standard −8.54 1116593 422889 230329_s_at NUDT6 Standard −8.4 1130926 508741 201310_s_at C5orf13 Standard −8.39 1135685 371282 210776_x_at TCF3 Standard −8.39 1140520 11747 221741_s_at C20orf21 Standard −8.34 1119802 7370 202522_at PITPNB Standard −8.31 1096149 410205 222824_at NUDT5 Standard −8.23 1124786 22370 212847_at NEXN Standard −8.07 1098012 355669 225756_at CSNK1E Standard −7.89 1116317 526415 228661_s_at Standard −7.86 1109195 416155 238853_at Standard −7.71 1134880 168799 209265_s_at METTL3 Standard −7.66 1529298 136707 Lymph_Dx_016_at Standard −7.55 1128660 413071 219471_at C13orf18 Standard −7.55 1138973 11270 218097_s_at C10orf66 Standard −7.46 1127294 421986 217028_at CXCR4 Standard 7.47 1134270 352119 208284_x_at GGT1 Standard 7.48 1120743 79197 204440_at CD83 Standard 7.5 1098179 163725 225956_at LOC153222 Standard 7.55 1121400 223474 205599_at TRAF1 Standard 7.59 1114967 7905 223028_s_at SNX9 Standard 7.6 1122087 72927 206693_at IL7 Standard 7.64 1101905 170843 230345_at Standard 7.77 1120700 410745 204362_at SCAP2 Standard 7.8 1120572 84 204116_at IL2RG Standard 7.84 1098271 300670 226056_at CDGAP Standard 7.9 1115073 131315 223220_s_at BAL Standard 7.9 1133210 434374 205842_s_at JAK2 Standard 8 1129269 62919 220358_at SNFT Standard 8.01 1131940 1103 203085_s_at TGFB1 Standard 8.07 1098506 193400 226333_at IL6R Standard 8.13 1120601 441129 204166_at KIAA0963 Standard 8.21 1102540 434881 231093_at FCRH3 Standard 8.24 1121695 511759 206082_at HCP5 Standard 8.33 1136877 409934 212998_x_at HLA-DQB1 Standard 8.37 1100138 278391 228234_at TIRP Standard 8.46 1126293 504816 215346_at TNFRSF5 Standard 8.46 1127805 380627 217947_at CKLFSF6 Standard 8.59 1136573 914 211991_s_at HLA-DPA1 Standard 8.62 1119111 35052 200804_at TEGT Standard 8.7 1136329 132739 211675_s_at HIC Standard 8.74 1123690 111805 210176_at TLR1 Standard 8.81 1138677 390440 217436_x_at Standard 8.89 1113993 131811 244286_at Standard 8.89 1132651 439767 204529_s_at TOX Standard 8.91 1119566 433506 201954_at ARPC1B Standard 9.01 1128626 501452 219424_at EBI3 Standard 9.17 1101272 179089 229584_at DKFZp434H2111 Standard 9.33 1136777 387679 212671_s_at HLA-DQA1 Standard 9.33 1109756 530304 239453_at Standard 9.4 1136216 512152 211528_x_at HLA-G Standard 9.4 1124381 440808 212288_at FNBP1 Standard 9.46 1099680 210387 227677_at JAK3 Standard 9.49 1109913 355724 239629_at CFLAR Standard 9.55 1132636 306278 204490_s_at CD44 Standard 9.59 1119243 440165 201171_at ATP6V0E Standard 9.72 1101149 517226 229437_at BIC Standard 9.8 1130674 381008 200905_x_at HLA-E Standard 10.34 1119939 170087 202820_at AHR Standard 10.44 1132883 432453 205027_s_at MAP3K8 Standard 10.74 1121452 227817 205681_at BCL2A1 Standard 10.84 1137360 429658 214196_s_at CLN2 Standard 12.08 1132520 283063 204249_s_at LMO2 Standard 12.33 1131497 114931 202295_s_at CTSH Standard 13.58 1123163 421342 208991_at STAT3 Lymph Node −9.1 1138136 433574 216215_s_at RBM9 Lymph Node 8.78 1130121 411958 221978_at HLA-F Lymph Node 9.22 1139830 221851 220330_s_at SAMSN1 Lymph Node 9.23 1131705 386467 202638_s_at ICAM1 Lymph Node 9.62 1130168 75626 222061_at CD58 Lymph Node 9.66 1121844 83077 206295_at IL18 Lymph Node 9.68 1121000 519033 204924_at TLR2 Lymph Node 9.83 1102437 437023 230966_at IL4I1 Lymph Node 10.71 1119475 296323 201739_at SGK Lymph Node 11.09 1131786 375957 202803_s_at ITGB2 Proliferation −11.07 1133141 344524 205677_s_at DLEU1 Proliferation −10.04 1138259 89525 216484_x_at HDGF Proliferation −9.74 1131578 202453 202431_s_at MYC Proliferation −9.45 1137449 223745 214363_s_at MATR3 Proliferation −9.43 1130468 166463 200594_x_at HNRPU Proliferation −9.21 1138157 82563 216251_s_at KIAA0153 Proliferation −9.15 1127756 313544 217850_at NS Proliferation −9 1130433 246112 200058_s_at U5-200KD Proliferation −8.76 1123108 108112 208828_at POLE3 Proliferation −8.75 1128738 335550 219581_at MGC2776 Proliferation −8.74 1122400 439911 207199_at TERT Proliferation −8.66 1097948 69476 225684_at LOC348235 Proliferation −8.6 1119460 76122 201696_at SFRS4 Proliferation −8.6 1136401 27258 211761_s_at SIP Proliferation −8.58 1099088 14355 226996_at Proliferation −8.51 1134653 253536 208901_s_at TOP1 Proliferation −8.49 1140584 294083 221932_s_at C14orf87 Proliferation −8.43 1121309 23642 205449_at HSU79266 Proliferation −8.43 1120385 36708 203755_at BUB1B Proliferation −8.38 1136710 75782 212429_s_at GTF3C2 Proliferation −8.36 1136605 448398 212064_x_at MAZ Proliferation −8.24 1120697 323462 204355_at DHX30 Proliferation −8.19 1127833 382044 218001_at MRPS2 Proliferation −8.11 1096903 437460 224185_at FLJ10385 Proliferation −8.1 1120596 4854 204159_at CDKN2C Proliferation −8.1 1120779 28853 204510_at CDC7 Standard Lymph Node Proliferation Mean BL 1098.69 576.05 −2392.12 Cut 1 0.09 Mean GCB 2187.37 768.53 −2129.35 Cut 2 0.53 Covariance BL 75263.67 12684.43 15734.77 12684.43 2650.81 2358.05 15734.77 2358.05 4653.00 Covariance GCB 50548.22 9301.12 14182.83 9301.12 2602.51 3028.21 14182.83 3028.21 5983.04

TABLE 2403 BL vs. PMBL Signature Scale UNIQID Unigene ID Build 167 Probe set Gene Symbol Standard −13.54 1099444 434489 227407_at FLJ90013 Standard −13.42 1096503 21379 223522_at C9orf45 Standard −13.36 1130114 445084 221965_at MPHOSPH9 Standard −13.27 1124786 22370 212847_at NEXN Standard −13.27 1134582 78202 208794_s_at SMARCA4 Standard −12.37 1096149 410205 222824_at NUDT5 Standard −11.95 1130855 77515 201189_s_at ITPR3 Standard −11.66 1529298 136707 Lymph_Dx_016_at Standard −11.35 1131246 153752 201853_s_at CDC25B Standard −11.17 1136925 436939 213154_s_at BICD2 Standard −11.08 1124188 282346 211987_at TOP2B Standard −11.06 1133998 76884 207826_s_at ID3 Standard −10.76 1139266 76640 218723_s_at RGC32 Standard −10.74 1134880 168799 209265_s_at METTL3 Standard −10.69 1140520 11747 221741_s_at C20orf21 Standard −10.6 1109545 63187 239231_at Standard −10.55 1106043 266331 235372_at FREB Standard −10.52 1110214 144519 239964_at TCL6 Standard −10.49 1098592 283707 226431_at ALS2CR13 Standard −10.45 1109220 445977 238880_at GTF3A Standard −10.41 1131263 249955 201877_s_at PPP2R5C Standard 10.54 1122772 66742 207900_at CCL17 Standard 10.59 1109913 355724 239629_at CFLAR Standard 10.82 1119884 418004 202716_at PTPN1 Standard 10.83 1135189 137569 209863_s_at TP73L Standard 10.89 1123437 73090 209636_at NFKB2 Standard 11.15 1124381 440808 212288_at FNBP1 Standard 11.26 1108237 126232 237753_at Standard 11.34 1101149 517226 229437_at BIC Standard 11.77 1139774 15827 220140_s_at SNX11 Standard 11.87 1123163 421342 208991_at STAT3 Standard 11.93 1129269 62919 220358_at SNFT Standard 12.03 1132636 306278 204490_s_at CD44 Standard 12.1 1138677 390440 217436_x_at Standard 12.2 1139950 437385 220731_s_at FLJ10420 Standard 12.25 1134270 352119 208284_x_at GGT1 Standard 12.27 1136216 512152 211528_x_at HLA-G Standard 12.79 1121400 223474 205599_at TRAF1 Standard 12.82 1119939 170087 202820_at AHR Standard 13.12 1126293 504816 215346_at TNFRSF5 Standard 13.44 1100138 278391 228234_at TIRP Standard 13.74 1132883 432453 205027_s_at MAP3K8 Standard 13.94 1131497 114931 202295_s_at CTSH Standard 14.15 1121762 32970 206181_at SLAMF1 Standard 14.51 1132520 283063 204249_s_at LMO2 Standard 14.68 1121452 227817 205681_at BCL2A1 Standard 15.24 1105178 283961 234284_at GNG8 Lymph Node 10.95 1121205 2488 205269_at LCP2 Lymph Node 11.22 1140845 21486 AFFX- STAT1 HUMISGF3A/M 97935_3_at Lymph Node 11.45 1131068 118400 201564_s_at FSCN1 Lymph Node 11.92 1131705 386467 202638_s_at ICAM1 Lymph Node 12.06 1131038 81328 201502_s_at NFKBIA Lymph Node 12.49 1121444 153563 205668_at LY75 Lymph Node 13.01 1123457 446304 209684_at RIN2 Lymph Node 13.19 1140404 354740 221584_s_at KCNMA1 Lymph Node 13.26 1124875 18166 212975_at KIAA0870 Lymph Node 14.06 1102437 437023 230966_at IL4I1 Lymph Node 14.11 1132766 82359 204781_s_at TNFRSF6 Lymph Node 15.31 1121767 458324 206187_at PTGIR Lymph Node 15.32 1135871 104717 211031_s_at CYLN2 Lymph Node 15.34 1138652 444471 217388_s_at KYNU Lymph Node 16.01 1139830 221851 220330_s_at SAMSN1 Standard Lymph Node Mean BL −66.97 1445.63 Cut 1 0.20 Mean PMBL 1205.38 2041.25 Cut 2 0.80 Covariance BL 35263.67 13424.88 13424.88 7458.56 Covariance PMBL 12064.38 5113.74 5113.74 3216.53

TABLE 2404 FH vs. DLBCL-BL Signature Scale UNIQID Unigene ID Build 167 Probe set Gene Symbol Standard −12.81 1104910 458262 233969_at IGL@ Standard −11.54 1102898 145519 231496_at FKSG87 Standard −11.46 1117298 449586 234366_x_at Standard −11.46 1132973 169294 205255_x_at TCF7 Standard −11.22 1133099 88646 205554_s_at DNASE1L3 Standard −10.76 1131531 153647 202350_s_at MATN2 Standard −10.59 1124283 406612 212144_at UNC84B Standard −10.35 1099847 36723 227867_at LOC129293 Standard −10.22 1136430 102950 211798_x_at IGLJ3 Standard −10.05 1117394 −13 234792_x_at Standard −9.95 1133047 528338 205434_s_at AAK1 Standard −9.95 1098865 250905 226741_at LOC51234 Standard −9.82 1108515 98132 238071_at LCN6 Standard −9.8 1131407 154248 202125_s_at ALS2CR3 Standard −9.77 1128469 390817 219173_at FLJ22686 Standard −9.7 1123875 428 210607_at FLT3LG Standard −9.69 1131875 169172 202965_s_at CAPN6 Standard −9.69 1135173 3781 209841_s_at LRRN3 Standard −9.48 1099798 411081 227811_at FGD3 Standard −9.41 1119046 349499 200606_at DSP Standard −9.36 1122449 278694 207277_at CD209 Standard −9.34 1114017 133255 244313_at Standard −9.34 1122767 652 207892_at TNFSF5 Standard −9.24 1123369 79025 209481_at SNRK Standard −9.16 1098954 128905 226844_at MOBKL2B Standard −9.14 1135513 421437 210481_s_at CD209L Standard −9.08 1100904 426296 229145_at LOC119504 Standard −8.99 1122738 81743 207840_at CD160 Standard −8.94 1120925 204891 204773_at IL11RA Standard 9.09 1123055 185726 208691_at TFRC Standard 9.62 1134858 405954 209226_s_at TNPO1 Standard 10.19 1123052 180909 208680_at PRDX1 Standard 10.81 1124178 446579 211969_at HSPCA Lymph Node −10.59 1137597 3903 214721_x_at CDC42EP4 Lymph Node −9.69 1119684 439586 202242_at TM4SF2 Lymph Node −9.25 1125593 8910 214180_at MAN1C1 Lymph Node −8.44 1124318 21858 212190_at SERPINE2 Lymph Node −8.09 1119448 212296 201656_at ITGA6 Lymph Node −8.07 1125546 125036 214081_at PLXDC1 Lymph Node −7.7 1097683 132569 225373_at PP2135 Lymph Node −7.56 1101305 112742 229623_at Lymph Node 7.45 1135240 436852 209955_s_at FAP Proliferation 6.97 1135101 20830 209680_s_at KIFC1 Proliferation 7.03 1130426 432607 200039_s_at PSMB2 Proliferation 7.04 1130501 2795 200650_s_at LDHA Proliferation 7.08 1130744 158688 201027_s_at EIF5B Proliferation 7.23 1137506 75258 214501_s_at H2AFY Proliferation 7.32 1131474 95577 202246_s_at CDK4 Proliferation 7.39 1130871 159087 201222_s_at RAD23B Proliferation 7.42 1119375 381072 201489_at PPIF Proliferation 7.47 1136595 404814 212038_s_at VDAC1 Proliferation 7.7 1135858 90093 211015_s_at HSPA4 Proliferation 7.78 1130527 184233 200692_s_at HSPA9B Proliferation 7.78 1130820 151777 201144_s_at EIF2S1 Proliferation 7.83 1115829 433213 225253_s_at METTL2 Proliferation 7.84 1134699 439683 208974_x_at KPNB1 Proliferation 7.87 1120274 31584 203517_at MTX2 Proliferation 7.92 1136786 63788 212694_s_at PCCB Proliferation 7.95 1097172 434886 224753_at CDCA5 Proliferation 8.4 1138537 −12 217140_s_at Proliferation 8.53 1119488 154672 201761_at MTHFD2 Proliferation 8.58 1130799 233952 201114_x_at PSMA7 Proliferation 8.72 1135673 82159 210759_s_at PSMA1 Proliferation 9.4 1114679 16470 222503_s_at FLJ10904 Standard Lymph Node Proliferation Mean FH −2193.59 −588.21 1571.78 Cut 1 0.50 Mean DLBCL-BL −1448.27 −441.91 1735.00 Cut 2 0.92 Covariance FH 6729.73 1223.99 2541.22 1223.99 405.22 293.72 2541.22 293.72 1797.58 Covariance DLBCL-BL 17675.23 3642.41 4158.43 3642.41 1379.81 1066.48 4158.43 1066.48 2858.21

TABLE 2405 FH vs. FL Signature Scale UNIQID Unigene ID Build 167 Probe set Gene Symbol Standard −11.23 1117298 449586 234366_x_at Standard −10.62 1121953 38365 206478_at KIAA0125 Standard −10.6 1104910 458262 233969_at IGL@ Standard −10.39 1136430 102950 211798_x_at IGLJ3 Standard −9.96 1129281 395486 220377_at C14orf110 Standard −9.73 1118835 102336 47069_at ARHGAP8 Standard −9.21 1127807 7236 217950_at NOSIP Standard −9.05 1128377 371003 219014_at PLAC8 Standard −8.85 1101004 2969 229265_at SKI Standard 9.06 1139411 368238 219073_s_at OSBPL10 Standard 9.07 1120789 154729 204524_at PDPK1 Standard 9.21 1136464 159428 211833_s_at BAX Standard 9.29 1125279 445652 213575_at TRA2A Standard 9.45 1529390 79241 Lymph_Dx_120_at BCL2 Standard 9.52 1132022 173911 203247_s_at ZNF24 Standard 9.57 1139645 134051 219757_s_at C14orf101 Standard 9.64 1137561 67397 214639_s_at HOXA1 Standard 9.66 1114893 314623 222891_s_at BCL11A Standard 10.38 1098095 131059 225852_at ANKRD17 Standard 10.4 1134858 405954 209226_s_at TNPO1 Standard 12.65 1101054 173328 229322_at PPP2R5E Standard 12.79 1124178 446579 211969_at HSPCA Standard 13.34 1135489 288178 210438_x_at SSA2 Standard Mean FH 136.43 Cut 1 0.50 Mean FL 640.38 Cut 2 0.99 Covariance FH 10719.40 Covariance FL 9373.11

TABLE 2406 FH vs. MCL Signature Scale UNIQID Unigene ID Build 167 Probe set Gene Symbol Standard 13.05 1100258 88442 228377_at KIAA1384 Standard 13.43 1529382 371468 Lymph_Dx_111_at CCND1 Standard 13.54 1106855 455101 236255_at KIAA1909 Standard 13.73 1529308 193014 Lymph_Dx_027_x_at Standard 14.56 1100873 445884 229103_at Standard 21.12 1132834 432638 204914_s_at SOX11 Lymph Node −8.44 1130378 234434 44783_s_at HEY1 Lymph Node −7.92 1123552 423077 209879_at SELPLG Lymph Node −7.7 1131218 76753 201809_s_at ENG Lymph Node −7.4 1097683 132569 225373_at PP2135 Lymph Node −7.15 1136273 13775 211597_s_at HOP Lymph Node 14.16 1134532 371468 208711_s_at CCND1 Standard Lymph Node Mean FH 451.68 −282.65 Cut 1 0.20 Mean MCFL 863.16 −156.82 Cut 2 0.80 Covariance FH 1617.92 222.89 222.89 271.65 Covariance MCL 3154.38 917.30 917.30 659.94

TABLE 2407 FH vs. SLL Signature Scale UNIQID Unigene ID Build 167 Probe set Gene Symbol Standard −13.14 1120765 343329 204484_at PIK3C2B Standard −12.9 1097897 266175 225622_at PAG Standard 12.72 1133195 274243 205805_s_at ROR1 Standard 12.74 1140416 58831 221601_s_at TOSO Standard 13.53 1131687 369280 202606_s_at TLK1 Standard 13.57 1107044 163426 236458_at Standard 14.43 1529389 79241 Lymph_Dx_119_at BCL2 Standard 14.51 1129026 135146 220007_at FLJ13984 Standard 14.77 1136987 21695 213370_s_at SFMBT1 Standard 14.79 1137109 469653 213689_x_at RPL5 Standard 15.37 1529308 193014 Lymph_Dx_027_x_at Standard 15.82 1120832 57856 204604_at PFTK1 Standard 17.37 1135550 221811 210550_s_at RASGRF1 Standard 18.98 1122864 434384 208195_at TTN Lymph Node −12.89 1123038 119000 208636_at ACTN1 Lymph Node −12.8 1130378 234434 44783_s_at HEY1 Lymph Node −11.59 1124875 18166 212975_at KIAA0870 Lymph Node −11.47 1103497 50115 232231_at Lymph Node −10.31 1099358 93135 227300_at Lymph Node −10.27 1121129 285401 205159_at CSF2RB Lymph Node −10.23 1100249 388674 228367_at HAK Lymph Node −10.05 1132345 109225 203868_s_at VCAM1 Lymph Node −9.93 1123401 50130 209550_at NDN Lymph Node −9.75 1120500 82568 203979_at CYP27A1 Lymph Node −9.57 1124318 21858 212190_at SERPINE2 Lymph Node −9.48 1120288 17483 203547_at CD4 Lymph Node −9.45 1123372 195825 209487_at RBPMS Lymph Node −9.39 1123376 37682 209496_at RARRES2 Lymph Node −9.29 1123213 12956 209154_at TIP-1 Lymph Node −9.23 1098412 409515 226225_at MCC Lymph Node −9.23 1125593 8910 214180_at MAN1C1 Lymph Node −9.17 1131786 375957 202803_s_at ITGB2 Lymph Node −9.04 1097683 132569 225373_at PP2135 Lymph Node −8.91 1097255 380144 224861_at Lymph Node −8.76 1131068 118400 201564_s_at FSCN1 Lymph Node −8.7 1119074 54457 200675_at CD81 Lymph Node −8.68 1125130 35861 213338_at RIS1 Lymph Node −8.59 1139661 416456 219806_s_at FN5 Standard Lymph Node Mean FH 1144.02 −2223.71 Cut 1 0.20 Mean SLL 1592.27 −1798.11 Cut 2 0.80 Covariance FH 902.56 442.69 442.69 809.90 Covariance SLL 2426.26 2938.58 2938.58 9435.72

TABLE 2408 FL vs. DLBCL-BL Signature Scale UNIQID Unigene ID Build 167 Probe set Gene Symbol Standard −23.03 1124833 356416 212914_at CBX7 Standard −22.25 1099204 193784 227121_at Standard −22.2 1119766 93231 202423_at MYST3 Standard −22.04 1099798 411081 227811_at FGD3 Standard −22.01 1102898 145519 231496_at FKSG87 Standard −21.79 1131197 269902 201778_s_at KIAA0494 Standard −21.69 1098415 130900 226230_at KIAA1387 Standard −21.57 1120834 57907 204606_at CCL21 Standard −21.39 1130155 436657 222043_at CLU Standard −20.98 1100904 426296 229145_at LOC119504 Standard −20.8 1131531 153647 202350_s_at MATN2 Standard −20.72 1137582 433732 214683_s_at CLK1 Standard −20.66 1119782 155418 202478_at TRB2 Standard −20.59 1122767 652 207892_at TNFSF5 Standard −20.58 1125001 16193 213158_at Standard −20.56 1134921 413513 209341_s_at IKBKB Standard −20.56 1132973 169294 205255_x_at TCF7 Standard −20.53 1136984 498154 213364_s_at SNX1 Standard −20.41 1115888 35096 225629_s_at ZBTB4 Standard −20.37 1120160 436976 203288_at KIAA0355 Standard −20.36 1139054 25726 218263_s_at LOC58486 Standard −20.31 1130030 301872 221834_at LONP Standard −20.08 1133024 436987 205383_s_at ZNF288 Standard −20.05 1124666 526394 212672_at ATM Standard −19.3 1529397 406557 Lymph_Dx_127_s_at CLK4 Standard −19.16 1116056 243678 226913_s_at SOX8 Standard −19.14 1098433 202577 226250_at Standard −19.1 1123635 408614 210073_at SIAT8A Standard −18.95 1138920 24395 218002_s_at CXCL14 Standard −18.84 1133099 88646 205554_s_at DNASE1L3 Standard −18.83 1098495 443668 226318_at TBRG1 Standard −18.64 1100879 119983 229111_at MASP2 Standard −18.59 1120695 385685 204352_at TRAF5 Standard −18.55 1119983 409783 202920_at ANK2 Standard −18.5 1101276 1098 229588_at ERdj5 Standard −18.47 1099140 500350 227052_at Standard −18.46 1529331 374126 Lymph_Dx_051_s_at Standard −18.45 1131752 170133 202724_s_at FOXO1A Standard −18.45 1099265 375762 227193_at Standard −18.32 1098179 163725 225956_at LOC153222 Standard −18.29 1119568 269777 201957_at PPP1R12B Standard −18.19 1099900 444508 227934_at Standard −18.17 1119361 391858 201448_at TIA1 Standard −18.02 1121650 421137 206002_at GPR64 Standard −17.91 1100911 320147 229152_at C4orf7 Standard −17.86 1529285 348929 Lymph_Dx_002_at KIAA1219 Standard −17.47 1529357 444651 Lymph_Dx_081_at Standard −17.42 1131863 2316 202936_s_at SOX9 Standard −17.16 1129943 512828 221626_at ZNF506 Standard −17.12 1121301 449971 205437_at ZNF134 Standard −17.11 1131340 437457 202018_s_at LTF Standard −17.1 1124606 444324 212588_at PTPRC Standard −17.08 1131407 154248 202125_s_at ALS2CR3 Standard −16.97 1118939 198161 60528_at PLA2G4B Standard −16.91 1134738 75842 209033_s_at DYRK1A Standard −16.9 1134083 285091 207996_s_at C18orf1 Standard −16.89 1120925 204891 204773_at IL11RA Standard −16.86 1110070 −101 239803_at Standard −16.83 1100042 351413 228113_at RAB37 Standard −16.82 1120134 75545 203233_at IL4R Standard −16.75 1124283 406612 212144_at UNC84B Standard −16.72 1109603 −100 239292_at Standard −16.71 1120509 155090 204000_at GNB5 Standard −16.65 1133538 1416 206760_s_at FCER2 Standard −16.64 1130735 179526 201009_s_at TXNIP Standard −16.59 1100150 9343 228248_at MGC39830 Standard −16.54 1124237 258855 212080_at MLL Standard −16.51 1124416 283604 212331_at RBL2 Standard −16.48 1133091 73792 205544_s_at CR2 Standard −16.46 1131263 249955 201877_s_at PPP2R5C Standard −16.44 1118347 528404 243366_s_at ITGA4 Standard −16.43 1529343 521948 Lymph_Dx_064_at Standard −16.43 1099549 446665 227533_at Standard 17.05 1529453 372679 Lymph_Dx_085_at FCGR3A Standard 17.41 1097540 388087 225195_at Standard 18.47 1140473 17377 221676_s_at CORO1C Standard 18.55 1121100 301921 205098_at CCR1 Standard 20.07 1124254 301743 212110_at SLC39A14 Standard 20.2 1130771 61153 201068_s_at PSMC2 Standard 21.46 1137583 273415 214687_x_at ALDOA Standard 21.55 1098168 22151 225943_at NLN Standard 24.07 1123055 185726 208691_at TFRC Standard 24.09 1123052 180909 208680_at PRDX1 Lymph Node −20.5 1137597 3903 214721_x_at CDC42EP4 Lymph Node −18.52 1124318 21858 212190_at SERPINE2 Lymph Node −18.5 1136762 380138 212624_s_at CHN1 Lymph Node −18.07 1101305 112742 229623_at Lymph Node −17.75 1100249 388674 228367_at HAK Lymph Node −16.1 1098412 409515 226225_at MCC Lymph Node −15.51 1140464 111676 221667_s_at HSPB8 Lymph Node −15.43 1136832 434959 212842_x_at RANBP2L1 Lymph Node −15.37 1119684 439586 202242_at TM4SF2 Lymph Node −15.02 1097448 250607 225093_at UTRN Lymph Node −14.83 1136844 16007 212875_s_at C21orf25 Lymph Node −14.73 1135056 169946 209604_s_at GATA3 Lymph Node −14.48 1097202 386779 224796_at DDEF1 Lymph Node −14.44 1121278 21355 205399_at DCAMKL1 Lymph Node −14.22 1125009 27621 213169_at Lymph Node −13.97 1100288 26981 228411_at ALS2CR19 Lymph Node −13.51 1132462 14845 204131_s_at FOXO3A Lymph Node −13.37 1135322 450230 210095_s_at IGFBP3 Lymph Node −13.35 1097280 423523 224891_at Lymph Node −12.86 1137097 20107 213656_s_at KNS2 Lymph Node −12.85 1098809 359394 226682_at Lymph Node −12.28 1124875 18166 212975_at KIAA0870 Lymph Node −12.18 1132345 109225 203868_s_at VCAM1 Lymph Node −12 1097561 19221 225224_at DKFZP566G1424 Lymph Node −11.71 1123401 50130 209550_at NDN Lymph Node −11.04 1136996 283749 213397_x_at RNASE4 Lymph Node −10.77 1136788 355455 212698_s_at 36778 Lymph Node −10.71 1098822 443452 226695_at PRRX1 Lymph Node −10.63 1134200 90786 208161_s_at ABCC3 Lymph Node −10.47 1136427 276506 211795_s_at FYB Lymph Node −10.46 1121186 100431 205242_at CXCL13 Lymph Node −10.39 1099332 32433 227272_at Lymph Node −10.39 1098978 124863 226869_at Lymph Node −10.22 1103303 49605 232000_at C9orf52 Lymph Node −10.16 1131325 13313 201990_s_at CREBL2 Lymph Node −10.16 1098174 274401 225949_at LOC340371 Lymph Node −9.93 1124733 66762 212771_at LOC221061 Lymph Node −9.42 1123372 195825 209487_at RBPMS Lymph Node −9.36 1132220 448805 203632_s_at GPRC5B Lymph Node −9.29 1120703 83974 204368_at SLCO2A1 Lymph Node −9.26 1132013 434961 203232_s_at SCA1 Lymph Node −9.25 1097307 379754 224929_at LOC340061 Lymph Node −9.18 1119251 433941 201194_at SEPW1 Lymph Node −9.08 1097609 6093 225283_at ARRDC4 Lymph Node −9.07 1136459 252550 211828_s_at KIAA0551 Lymph Node −8.86 1132775 1027 204803_s_at RRAD Lymph Node −8.78 1098946 135121 226834_at ASAM Lymph Node −8.68 1140589 433488 221942_s_at GUCY1A3 Lymph Node −8.44 1116966 301124 232744_x_at Lymph Node −8.39 1100130 76494 228224_at PRELP Lymph Node −8.36 1110019 −94 239744_at Lymph Node −8.3 1134647 298654 208892_s_at DUSP6 Lymph Node −8.28 1125593 8910 214180_at MAN1C1 Lymph Node 7.97 1134370 1422 208438_s_at FGR Lymph Node 8.05 1123566 155935 209906_at C3AR1 Lymph Node 8.09 1131119 349656 201647_s_at SCARB2 Lymph Node 8.11 1123586 93841 209948_at KCNMB1 Lymph Node 8.13 1128615 104800 219410_at FLJ10134 Lymph Node 8.21 1097297 166254 224917_at VMP1 Lymph Node 8.23 1120299 79334 203574_at NFIL3 Lymph Node 8.37 1128157 23918 218631_at VIP32 Lymph Node 8.4 1130054 82547 221872_at RARRES1 Lymph Node 8.41 1098152 377588 225922_at KIAA1450 Lymph Node 8.53 1101566 98558 229947_at Lymph Node 8.59 1135251 21486 209969_s_at STAT1 Lymph Node 8.84 1099167 381105 227080_at MGC45731 Lymph Node 9.01 1132920 753 205119_s_at FPR1 Lymph Node 9.26 1097253 77873 224859_at B7H3 Lymph Node 9.29 1120500 82568 203979_at CYP27A1 Lymph Node 9.36 1131507 172928 202311_s_at COL1A1 Lymph Node 9.38 1096456 82407 223454_at CXCL16 Lymph Node 9.49 1136172 38084 211470_s_at SULT1C1 Lymph Node 10.03 1138244 418138 216442_x_at FN1 Lymph Node 10.34 1134424 −17 208540_x_at S100A14 Lymph Node 10.48 1136152 458436 211434_s_at CCRL2 Lymph Node 10.51 1118708 7835 37408_at MRC2 Lymph Node 10.6 1136540 179657 211924_s_at PLAUR Lymph Node 10.63 1098278 166017 226066_at MITF Lymph Node 10.76 1119477 163867 201743_at CD14 Lymph Node 10.81 1096429 64896 223405_at NPL Lymph Node 11.58 1123672 67846 210152_at LILRB4 Lymph Node 12 1096364 29444 223276_at NID67 Lymph Node 12.16 1119070 445570 200663_at CD63 Lymph Node 12.3 1133065 77274 205479_s_at PLAU Lymph Node 12.5 1135240 436852 209955_s_at FAP Lymph Node 13.09 1116826 26204 231823_s_at KIAA1295 Lymph Node 13.32 1119068 417004 200660_at S100A11 Lymph Node 13.45 1120266 246381 203507_at CD68 Lymph Node 13.63 1133216 502577 205872_x_at PDE4DIP Lymph Node 13.67 1131815 386678 202856_s_at SLC16A3 Lymph Node 14.38 1132132 279910 203454_s_at ATOX1 Lymph Node 15.25 1134682 411701 208949_s_at LGALS3 Lymph Node 15.46 1119237 389964 201141_at GPNMB Lymph Node 15.89 1137698 442669 215001_s_at GLUL Lymph Node 17.8 1137782 384944 215223_s_at SOD2 Lymph Node 20.11 1130629 135226 200839_s_at CTSB Proliferation 21.02 1119375 381072 201489_at PPIF Proliferation 21.24 1119488 154672 201761_at MTHFD2 Proliferation 21.31 1119467 21635 201714_at TUBG1 Proliferation 21.68 1130820 151777 201144_s_at EIF2S1 Proliferation 21.69 1131474 95577 202246_s_at CDK4 Proliferation 22.2 1125249 244723 213523_at CCNE1 Proliferation 22.97 1130501 2795 200650_s_at LDHA Proliferation 23.12 1136913 99962 213113_s_at SLC43A3 Proliferation 24.05 1130426 432607 200039_s_at PSMB2 Standard Lymph Node Proliferation Mean FL −11121.51 −1603.39 1890.60 Cut 1 0.34 Mean DLBCL-BL −8760.65 −460.71 2101.10 Cut 2 0.94 Covariance FL 246359.77 111505.42 28908.20 111505.42 67036.17 13130.59 28908.20 13130.59 4617.24 Covariance DLBCL-BL 413069.12 178811.32 30151.89 178811.32 106324.53 10877.26 30151.89 10877.26 5180.68

TABLE 2409 FL vs. MCL Signature Scale UNIQID Unigene ID Build 167 Probe set Gene Symbol Standard −24.56 1123731 17165 210258_at RGS13 Standard −22.56 1133192 24024 205801_s_at RASGRP3 Standard −21.12 1114543 156189 244887_at Standard −18.49 1120090 155024 203140_at BCL6 Standard −18.07 1124646 436432 212646_at RAFTLIN Standard −17.24 1132122 307734 203434_s_at MME Standard −16.63 1105986 49614 235310_at GCET2 Standard −15.09 1120134 75545 203233_at IL4R Standard −14.05 1132651 439767 204529_s_at TOX Standard 13.8 1098277 6786 226065_at PRICKLE1 Standard 13.85 1109560 207428 239246_at FARP1 Standard 13.86 1103504 142517 232239_at Standard 13.88 1132734 126248 204724_s_at COL9A3 Standard 13.91 1115905 301478 225757_s_at CLMN Standard 14.89 1098840 55098 226713_at C3orf6 Standard 14.97 1100873 445884 229103_at Standard 14.99 1139393 170129 219032_x_at OPN3 Standard 16.13 1124864 411317 212960_at KIAA0882 Standard 16.36 1106855 455101 236255_at KIAA1909 Standard 16.43 1120858 410683 204647_at HOMER3 Standard 17.38 1130926 508741 201310_s_at C5orf13 Standard 18.3 1103711 288718 232478_at Standard 18.62 1109505 8162 239186_at MGC39372 Standard 20.31 1132834 432638 204914_s_at SOX11 Standard 22.61 1096070 241565 222640_at DNMT3A Standard 28.66 1529382 371468 Lymph_Dx_111_at CCND1 Lymph Node −10.77 1097202 386779 224796_at DDEF1 Lymph Node −10.22 1119546 433898 201921_at GNG10 Lymph Node −9.89 1132766 82359 204781_s_at TNFRSF6 Lymph Node −9.4 1138867 10706 217892_s_at EPLIN Lymph Node 9.65 1125025 301094 213196_at Lymph Node 10.44 1134797 433394 209118_s_at TUBA3 Lymph Node 22.6 1529456 371468 Lymph_Dx_113_at CCND1 Proliferation −7.36 1097948 69476 225684_at LOC348235 Proliferation −7.31 1130747 234489 201030_x_at LDHB Proliferation −6.95 1130923 459987 201306_s_at ANP32B Proliferation −6.87 1120205 5198 203405_at DSCR2 Proliferation −6.64 1132468 79353 204147_s_at TFDP1 Proliferation −6.1 1119916 177584 202780_at OXCT Proliferation −6.08 1119873 446393 202697_at CPSF5 Proliferation −6.08 1119488 154672 201761_at MTHFD2 Proliferation −6.04 1130658 447492 200886_s_at PGAM1 Proliferation −5.82 1132825 512813 204900_x_at SAP30 Proliferation −5.53 1115607 435733 224428_s_at CDCA7 Proliferation −5.44 1120316 63335 203611_at TERF2 Proliferation −5.34 1114970 279529 223032_x_at PX19 Proliferation −5.32 1140843 169476 AFFX- GAPD HUMGAPDH/M 33197_5_at Proliferation −5.28 1131081 180610 201586_s_at SFPQ Proliferation −5.15 1121062 408658 205034_at CCNE2 Proliferation 5.15 1120986 172052 204886_at PLK4 Proliferation 5.16 1097195 149931 224785_at MGC29814 Proliferation 5.2 1120011 3068 202983_at SMARCA3 Proliferation 5.47 1100183 180582 228286_at FLJ40869 Proliferation 5.67 1121012 96055 204947_at E2F1 Proliferation 5.84 1115679 8345 224523_s_at MGC4308 Proliferation 5.88 1135285 449501 210024_s_at UBE2E3 Proliferation 5.92 1120520 35120 204023_at RFC4 Proliferation 6.16 1529361 388681 Lymph_Dx_086_s_at HDAC3 Proliferation 6.45 1096054 21331 222606_at FLJ10036 Proliferation 6.45 1096738 87968 223903_at TLR9 Proliferation 6.51 1136781 120197 212680_x_at PPP1R14B Proliferation 6.63 1119466 179718 201710_at MYBL2 Proliferation 6.65 1136285 182490 211615_s_at LRPPRC Proliferation 6.67 1136853 66170 212922_s_at SMYD2 Proliferation 7.45 1119390 77254 201518_at CBX1 Proliferation 8.87 1116122 42768 227408_s_at DKFZp761O0113 Proliferation 10.12 1119515 3352 201833_at HDAC2 Standard Lymph Node Proliferation Mean FL −18.82 −33.90 23.53 Cut 1 0.14 Mean MCL 1558.10 113.95 165.48 Cut 2 0.58 Covariance FL 21302.14 1098.24 678.04 1098.24 226.29 75.99 678.04 75.99 315.67 Covariance MCL 81008.29 5261.37 9185.20 5261.37 2047.34 875.56 9185.20 875.56 1447.43

TABLE 2410 FL vs. SLL Signature Scale UNIQID Unigene ID Build 167 Probe set Gene Symbol Standard −21.04 1123731 17165 210258_at RGS13 Standard −20.91 1124646 436432 212646_at RAFTLIN Standard −18.82 1099651 120785 227646_at EBF Standard −18.12 1114543 156189 244887_at Standard −17.85 1105986 49614 235310_at GCET2 Standard −16.73 1100911 320147 229152_at C4orf7 Standard −15.77 1132122 307734 203434_s_at MME Standard −15.12 1120090 155024 203140_at BCL6 Standard −14.89 1097897 266175 225622_at PAG Standard −14.36 1529343 521948 Lymph_Dx_064_at Standard −14.32 1529318 291954 Lymph_Dx_038_at Standard −14.06 1128694 171466 219517_at ELL3 Standard −13.61 1101586 187884 229971_at GPR114 Standard −13.57 1119752 511745 202391_at BASP1 Standard −13.13 1137561 67397 214639_s_at HOXA1 Standard −12.85 1097247 388761 224851_at CDK6 Standard −12.43 1529344 317970 Lymph_Dx_065_at SERPINA11 Standard −12.4 1120765 343329 204484_at PIK3C2B Standard −12.33 1130155 436657 222043_at CLU Standard −12.07 1529292 −92 Lymph_Dx_010_at Standard −12.01 1119939 170087 202820_at AHR Standard −11.82 1119919 199263 202786_at STK39 Standard −11.77 1099686 117721 227684_at Standard −11.63 1119782 155418 202478_at TRB2 Standard 10.97 1529309 512797 Lymph_Dx_028_at HSH2 Standard 10.97 1139393 170129 219032_x_at OPN3 Standard 11.04 1131246 153752 201853_s_at CDC25B Standard 11.07 1140391 44865 221558_s_at LEF1 Standard 11.16 1140416 58831 221601_s_at TOSO Standard 11.35 1127807 7236 217950_at NOSIP Standard 11.67 1529317 −98 Lymph_Dx_037_at Standard 11.81 1117343 306812 234643_x_at BUCS1 Standard 11.82 1102081 506977 230551_at Standard 11.82 1135042 79015 209582_s_at MOX2 Standard 11.96 1132734 126248 204724_s_at COL9A3 Standard 12.09 1137109 469653 213689_x_at RPL5 Standard 12.14 1099939 488173 227983_at MGC7036 Standard 12.19 1129103 99430 220118_at TZFP Standard 12.47 1135592 758 210621_s_at RASA1 Standard 12.78 1108970 140489 238604_at Standard 12.92 1097143 74335 224716_at HSPCB Standard 13.18 1136865 412128 212959_s_at MGC4170 Standard 13.96 1098220 80720 226002_at GAB1 Standard 14.06 1100847 97411 229070_at C6orf105 Standard 14.39 1098865 250905 226741_at LOC51234 Standard 15.57 1136687 59943 212345_s_at CREB3L2 Standard 15.75 1107044 163426 236458_at Standard 16.52 1123622 8578 210051_at EPAC Standard 17.74 1136987 21695 213370_s_at SFMBT1 Standard 19.15 1129026 135146 220007_at FLJ13984 Standard 19.65 1131854 414985 202923_s_at GCLC Lymph Node −14.99 1124875 18166 212975_at KIAA0870 Lymph Node −14.33 1099358 93135 227300_at Lymph Node −13.26 1121129 285401 205159_at CSF2RB Lymph Node −12.61 1119074 54457 200675_at CD81 Lymph Node −12.52 1121029 412999 204971_at CSTA Lymph Node −11.48 1137247 234734 213975_s_at LYZ Lymph Node −10.97 1128781 79741 219648_at FLJ10116 Lymph Node 11.79 1119880 442844 202709_at FMOD Lymph Node 14.4 1134370 1422 208438_s_at FGR Standard Lymph Node Mean FL −663.95 −730.08 Cut 1 0.20 Mean SLL 1332.84 −484.93 Cut 2 0.80 Covariance FL 37097.15 1710.73 1710.73 663.78 Covariance SLL 85989.25 17661.52 17661.52 4555.06

TABLE 2411 GCB vs. PMBL Signature Scale UNIQID Unigene ID Build 167 Probe set Gene Symbol Standard −8.39 1096440 231320 223423_at GPR160 Standard −8.13 1096108 292871 222731_at ZDHHC2 Standard −8.12 1125231 446375 213489_at MAPRE2 Standard −8.02 1136759 188882 212605_s_at Standard −7.91 1096499 293867 223514_at CARD11 Standard −7.8 1099388 124024 227336_at DTX1 Standard −7.71 1139623 193736 219667_s_at BANK1 Standard −7.68 1098592 283707 226431_at ALS2CR13 Standard −7.67 1107575 424589 237033_at MGC52498 Standard −7.63 1116829 115467 231840_x_at LOC90624 Standard −7.42 1130114 445084 221965_at MPHOSPH9 Standard −7.27 1098909 446408 226789_at Standard 7.34 1138759 396404 217707_x_at SMARCA2 Standard 7.37 1120355 80420 203687_at CX3CL1 Standard 7.4 1134270 352119 208284_x_at GGT1 Standard 7.44 1115441 5470 224156_x_at IL17RB Standard 7.78 1103054 341531 231690_at Standard 7.91 1119765 81234 202421_at IGSF3 Standard 7.92 1119438 118110 201641_at BST2 Standard 8.09 1135645 31439 210715_s_at SPINT2 Standard 8.15 1106015 96885 235343_at FLJ12505 Standard 8.18 1121400 223474 205599_at TRAF1 Standard 8.38 1139950 437385 220731_s_at FLJ10420 Standard 8.73 1122112 1314 206729_at TNFRSF8 Standard 8.77 1122772 66742 207900_at CCL17 Standard 8.84 1132762 80395 204777_s_at MAL Standard 9.64 1139774 15827 220140_s_at SNX11 Standard 10.53 1133801 181097 207426_s_at TNFSF4 Standard 11.52 1106415 169071 235774_at Standard 12.09 1129269 62919 220358_at SNFT Standard Mean GCB 292.76 Cut 1 0.16 Mean PMBL 725.28 Cut 2 0.50 Covariance GCB 8538.86 Covariance PMBL 11405.23

TABLE 2412 MCL vs. DLBCL-BL Signature Scale UNIQID Unigene ID Build 167 Probe set Gene Symbol Standard −26.11 1529382 371468 Lymph_Dx_111_at CCND1 Standard −18.35 1103711 288718 232478_at Standard −17.03 1106855 455101 236255_at KIAA1909 Standard −16.49 1098840 55098 226713_at C3orf6 Standard −15.41 1109505 8162 239186_at MGC39372 Standard −15.11 1098954 128905 226844_at MOBKL2B Standard −14.96 1103504 142517 232239_at Standard −14.74 1096070 241565 222640_at DNMT3A Standard −13.81 1137663 247362 214909_s_at DDAH2 Standard −13.8 1124864 411317 212960_at KIAA0882 Standard −13.62 1140127 125300 221044_s_at TRIM34 Standard −13.62 1119361 391858 201448_at TIA1 Standard −13.37 1127849 76691 218032_at SNN Standard 13.72 1133192 24024 205801_s_at RASGRP3 Standard 13.85 1137583 273415 214687_x_at ALDOA Standard 15.02 1123052 180909 208680_at PRDX1 Standard 16.21 1097611 438993 225285_at BCAT1 Lymph Node −19.18 1529456 371468 Lymph_Dx_113_at CCND1 Lymph Node −10.71 1098978 124863 226869_at Lymph Node −9.17 1097448 250607 225093_at UTRN Lymph Node 8.84 1135240 436852 209955_s_at FAP Lymph Node 9.11 1119475 296323 201739_at SGK Lymph Node 9.22 1119237 389964 201141_at GPNMB Lymph Node 9.46 1130629 135226 200839_s_at CTSB Lymph Node 10.1 1130054 82547 221872_at RARRES1 Standard Lymph Node Mean MCL −1417.55 −25.58 Cut 1 0.50 Mean DLBCL-BL −756.07 202.29 Cut 2 0.88 Covariance MCL 15347.98 3525.48 3525.48 5420.31 Covariance DLBCL-BL 5132.06 1007.64 1007.64 991.38

TABLE 2413 MCL vs. SLL Signature Scale UNIQID Unigene ID Build 167 Probe set Gene Symbol Standard −20.18 1132834 432638 204914_s_at SOX11 Standard −15.17 1130926 508741 201310_s_at C5orf13 Standard 13.44 1116150 16229 227606_s_at AMSH-LP Standard 14.44 1120134 75545 203233_at IL4R Standard 15.18 1529437 445162 Lymph_Dx_175_at BTLA Standard 15.19 1529317 −98 Lymph_Dx_037_at Standard 16.2 1135042 79015 209582_s_at MOX2 Standard Mean MCL 181.38 Cut 1 0.20 Mean SLL 564.92 Cut 2 0.80 Covariance MCL 1734.42 Covariance SLL 910.75

TABLE 2414 SLL vs. DLBCL-BL Signature Scale UNIQID Unigene ID Build 167 Probe set Gene Symbol Standard −16.014498 1123622 8578 210051_at EPAC Standard −15.26356533 1102081 506977 230551_at Standard −14.82150028 1107044 163426 236458_at Standard −14.17813266 1098865 250905 226741_at LOC51234 Standard −12.92844719 1110740 416810 240538_at Standard −12.86520757 1129026 135146 220007_at FLJ13984 Standard −12.2702748 1135592 758 210621_s_at RASA1 Standard −11.87309449 1117343 306812 234643_x_at BUCS1 Standard −11.81789137 1136987 21695 213370_s_at SFMBT1 Standard −11.78631706 1124830 9059 212911_at KIAA0962 Standard −11.39454435 1133538 1416 206760_s_at FCER2 Standard −11.39050362 1135802 439343 210944_s_at CAPN3 Standard 11.72928644 1120770 300825 204493_at BID Lymph Node −12.21593247 1119880 442844 202709_at FMOD Lymph Node 9.514704847 1135240 436852 209955_s_at FAP Lymph Node 9.739298877 1096429 64896 223405_at NPL Lymph Node 10.05087645 1119475 296323 201739_at SGK Lymph Node 13.11985922 1119237 389964 201141_at GPNMB Proliferation 10.47525875 1128106 14559 218542_at C10orf3 Proliferation 10.53295782 1132825 512813 204900_x_at SAP30 Proliferation 11.93918891 1130501 2795 200650_s_at LDHA Proliferation 11.98738778 1123439 287472 209642_at BUB1 Proliferation 11.99741644 1115607 435733 224428_s_at CDCA7 Standard Lymph Node Proliferation Mean SLL −1383.640809 177.4452398 467.2463569 Cut 1 0.201266305 Mean DLBCL-BL −926.7275468 329.6795845 582.9070266 Cut 2 0.799816116 Covariance SLL 3591.384775 1789.7516 856.0703202 1789.7516 1421.869535 663.4782048 856.0703202 663.4782048 965.6470151 Covariance DLBCL-BL 2922.643347 473.543487 634.3258773 473.543487 931.9845277 −53.85584619 634.3258773 −53.85584619 767.3545404

The following List of Materials submitted by Compact Disc is provided in accordance with 37 C.F.R. § 1.52(e)(5). Any reference to a table or file in the foregoing specification should be considered an incorporation by reference of the contents of the table and/or file at that particular place in the specification.

List of Materials Submitted by Compact Disc

Table No. File Name File size (bytes) Date Created Disc 1 of 22 2 Table_0002_LymphDx_Probe_List.txt 91,364 Aug. 31, 2004 3 Table_0003_ABC_304_A.txt 12,168,568 Aug. 10, 2004 4 Table_0004_ABC_304_B.txt 11,943,114 Aug. 10, 2004 5 Table_0005_ABC_305_A.txt 12,213,467 Aug. 10, 2004 6 Table_0006_ABC_305_B.txt 12,154,905 Aug. 10, 2004 7 Table_0007_ABC_309_A.txt 12,141,759 Aug. 10, 2004 8 Table_0008_ABC_309_B.txt 11,973,212 Aug. 10, 2004 9 Table_0009_ABC_413_A.txt 12,301,325 Aug. 10, 2004 10 Table_0010_ABC_413_B.txt 12,109,937 Aug. 10, 2004 11 Table_0011_ABC_428_A.txt 12,329,882 Aug. 10, 2004 12 Table_0012_ABC_428_B.txt 12,126,615 Aug. 10, 2004 13 Table_0013_ABC_432_A.txt 12,316,197 Aug. 10, 2004 14 Table_0014_ABC_432_B.txt 12,017,441 Aug. 10, 2004 15 Table_0015_ABC_446_A.txt 12,285,490 Aug. 10, 2004 16 Table_0016_ABC_446_B.txt 12,041,763 Aug. 10, 2004 17 Table_0017_ABC_462_A.txt 12,274,795 Aug. 10, 2004 18 Table_0018_ABC_452_B.txt 12,056,215 Aug. 10, 2004 19 Table_0019_ABC_477_A.txt 12,182,261 Aug. 10, 2004 20 Table_0020_ABC_477_B.txt 12,091,353 Aug. 10, 2004 21 Table_0021_ABC_481_A.txt 12,311,932 Aug. 10, 2004 22 Table_0022_ABC_481_B.txt 12,143,332 Aug. 10, 2004 23 Table_0023_ABC_482_A.txt 12,259,053 Aug. 10, 2004 24 Table_0024_ABC_482_B.txt 12,126,711 Aug. 10, 2004 25 Table_0025_ABC_538_A.txt 12,269,821 Aug. 10, 2004 26 Table_0026_ABC_538_B.txt 12,135,241 Aug. 10, 2004 27 Table_0027_ABC_541_A.txt 12,159,265 Aug. 11, 2004 28 Table_0028_ABC_541_B.txt 11,960,425 Aug. 11, 2004 29 Table_0029_ABC_544_A.txt 12,199,335 Aug. 11, 2004 30 Table_0030_ABC_544_B.txt 11,995,098 Aug. 11, 2004 31 Table_0031_ABC_547_A.txt 12,174,490 Aug. 11, 2004 32 Table_0032_ABC_547_B.txt 11,948,698 Aug. 11, 2004 33 Table_0033_ABC_577_A.txt 12,015,593 Aug. 11, 2004 34 Table_0034_ABC_577_B.txt 11,850,326 Aug. 11, 2004 35 Table_0035_ABC_616_A.txt 12,151,453 Aug. 11, 2904 36 Table_0036_ABC_616_B.txt 11,877,346 Aug. 11, 2004 37 Table_0037_ABC_626_A.txt 12,319,773 Aug. 11, 2004 38 Table_0038_ABC_626_B.txt 12,099,972 Aug. 11, 2004 39 Table_0039_ABC_633_A.txt 12,144,330 Aug. 11, 2004 40 Table_0040_ABC_633_B.txt 12,051,373 Aug. 11, 2004 41 Table_0041_ABC_642_A.txt 12,062,720 Aug. 11, 2004 42 Table_0042_ABC_642_B.txt 11,924,314 Aug. 11, 2004 43 Table_0043_ABC_644_A.txt 12,095,292 Aug. 11, 2004 44 Table_0044_ABC_644_B.txt 12,026,946 Aug. 11, 2004 45 Table_0045_ABC_645_A.txt 12,142,419 Aug. 11, 2004 46 Table_0046_ABC_645_B.txt 11,922,646 Aug. 11, 2004 47 Table_0047_ABC_646_A.txt 12,170,447 Aug. 11, 2004 48 Table_0048_ABC_646_B.txt 12,005,366 Aug. 11, 2004 49 Table_0049_ABC_651_A.txt 12,045,047 Aug. 11, 2004 50 Table_0050_ABC_651_B.txt 11,922,418 Aug. 11, 2004 51 Table_0051_ABC_652_A.txt 12,103,596 Aug. 11, 2004 52 Table_0052_ABC_652_B.txt 11,970,072 Aug. 11, 2004 53 Table_0053_ABC_660_A.txt 12,149,615 Aug. 11, 2004 54 Table_0054_ABC_660_B.txt 12,035,417 Aug. 11, 2004 55 Table_0055_ABC_663_A.txt 12,295,719 Aug. 11, 2004 56 Table_0056_ABC_663_B.txt 12,153,562 Aug. 11, 2004 Disc 2 of 22 57 Table_0057_ABC_668_A.txt 12,254,586 Aug. 11, 2004 58 Table_0058_ABC_668_B.txt 12,077,180 Aug. 11, 2004 59 Table_0059_ABC_676_A.txt 12,042,636 Aug. 11, 2004 60 Table_0060_ABC_676_B.txt 11,923,373 Aug. 11, 2004 61 Table_0061_ABC_678_A.txt 12,051,214 Aug. 11, 2004 62 Table_0002_ABC_678_B.txt 11,885,465 Aug. 11, 2004 63 Table_0063_ABC_687_A.txt 12,288,163 Aug. 11, 2004 64 Table_0064_ABC_687_B.txt 12,114,176 Aug. 11, 2004 65 Table_0065_ABC_689_A.txt 12,239,706 Aug. 11, 2004 66 Table_0066_ABC_689_B.txt 11,999,699 Aug. 11, 2004 67 Table_0067_ABC_692_A.txt 12,193,147 Aug. 11, 2004 68 Table_0068_ABC_692_B.txt 11,876,485 Aug. 11, 2004 69 Table_0069_ABC_694_A.txt 12,256,974 Aug. 11, 2004 70 Table_0070_ABC_694_B.txt 12,011,057 Aug. 11, 2004 71 Table_0071_ABC_700_A.txt 12,085,982 Aug. 11, 2004 72 Table_0072_ABC_700_B.txt 11,917,957 Aug. 11, 2004 73 Table_0073_ABC_702_A.txt 12,121,512 Aug. 11, 2004 74 Table_0074_ABC_702_B.txt 11,939,438 Aug. 11, 2004 75 Table_0075_ABC_704_A.txt 12,287,602 Aug. 11, 2004 76 Table_0076_ABC_704_B.txt 12,103,199 Aug. 11, 2004 77 Table_0077_ABC_709_A.txt 12,212,735 Aug. 11, 2004 78 Table_0078_ABC_709_B.txt 11,967,460 Aug. 11, 2004 79 Table_0079_ABC_712_A.txt 12,253,447 Aug. 11, 2004 80 Table_0080_ABC_712_B.txt 12,070,582 Aug. 11, 2004 81 Table_0081_ABC_714_A.txt 12,144,498 Aug. 11, 2004 82 Table_0082_ABC_714_B.txt 11,920,284 Aug. 11, 2004 83 Table_0083_ABC_717_A.txt 12,142,496 Aug. 11, 2004 84 Table_0084_ABC_717_B.txt 11,954,244 Aug. 11, 2004 85 Table_0085_ABC_725_A.txt 12,080,186 Aug. 11, 2004 86 Table_0086_ABC_725_B.txt 11,751,528 Aug. 11, 2004 87 Table_0087_ABC_726_A.txt 11,989,772 Aug. 11, 2004 88 Table_0088_ABC_726_B.txt 11,763,881 Aug. 11, 2004 89 Table_0089_ABC_730_A.txt 12,080,456 Aug. 11, 2004 90 Table_0090_ABC_730_B.txt 11,902,874 Aug. 11, 2004 91 Table_0091_ABC_753_A.txt 12,200,060 Aug. 11, 2004 92 Table_0092_ABC_753_B.txt 11,936,481 Aug. 11, 2004 93 Table_0093_ABC_756_A.txt 12,280,005 Aug. 11, 2004 94 Table_0094_ABC_756_B.txt 11,983,560 Aug. 11, 2004 95 Table_0095_ABC_771_A.txt 12,137,758 Aug. 11, 2004 96 Table_0096_ABC_771_B.txt 11,975,832 Aug. 11, 2004 97 Table_0097_ABC_779_A.txt 12,297,481 Aug. 11, 2004 98 Table_0098_ABC_779_B.txt 12,002,334 Aug. 11, 2004 99 Table_0099_ABC_789_A.txt 12,206,349 Aug. 11, 2004 100 Table_0100_ABC_789_B.txt 12,016,648 Aug. 11, 2004 101 Table_0101_ABC_800_A.txt 12,003,215 Aug. 11, 2004 102 Table_0102_ABC_800_B.txt 11,934,562 Aug. 11, 2004 103 Table_0103_ABC_807_A.txt 12,103,858 Aug. 11, 2004 104 Table_0104_ABC_807_B.txt 11,887,326 Aug. 11, 2004 105 Table_0105_ABC_809_A.txt 12,058,952 Aug. 11, 2004 106 Table_0106_ABC_809_B.txt 11,797,773 Aug. 11, 2004 107 Table_0107_ABC_816_A.txt 11,979,780 Aug. 11, 2004 108 Table_0108_ABC_816_B.txt 11,874,102 Aug. 11, 2004 109 Table_0109_ABC_820_A.txt 12,073,993 Aug. 11, 2004 110 Table_0110_ABC_820_B.txt 11,837,974 Aug. 11, 2004 Disc 3 of 22 111 Table_0111_ABC_823_A.txt 12,082,433 Aug. 11, 2004 112 Table_0112_ABC_823_B.txt 11,864,483 Aug. 11, 2004 113 Table_0113_ABC_835_A.txt 12,235,272 Aug. 11, 2004 114 Table_0114_ABC_835_B.txt 12,002,461 Aug. 11, 2004 115 Table_0115_ABC_839_A.txt 12,203,437 Aug. 11, 2004 116 Table_0116_ABC_839_B.txt 11,920,023 Aug. 11, 2004 117 Table_0117_ABC_841_A.txt 12,331,151 Aug. 11, 2004 118 Table_0118_ABC_841_B.txt 12,078,461 Aug. 11; 2004 119 Table_0119_ABC_858_A.txt 12,207,153 Aug. 11, 2004 120 Table_0120_ABC_858_B.txt 12,040,055 Aug. 11, 2004 121 Table_0121_ABC_872_A.txt 12,160,913 Aug. 11, 2004 122 Table_0122_ABC_872_B.txt 11,972,706 Aug. 11, 2004 123 Table_0123_ABC_875_A.txt 12,062,435 Aug. 11, 2Q04 124 Table_0124_ABC_875_B.txt 11,868,039 Aug. 11, 2004 125 Table_0125_ABC_912_A.txt 12,203,349 Aug. 11, 2004 126 Table_0126_ABC_912_B.txt 12,056,482 Aug. 11, 2004 127 Table_0127_ABC_996_A.txt 12,145,520 Aug. 11, 2004 128 Table_0128_ABC_996_B.txt 11,926,049 Aug. 11, 2004 129 Table_0129_ABC_1000_A.txt 12,172,512 Aug. 10, 2004 130 Table_0130_ABC_1000_B.txt 11,960,378 Aug. 10, 2004 131 Table_0131_ABC_1002_A.txt 12,136,443 Aug. 10, 2004 132 Table_0132_ABC_1002_B.txt 11,912,994 Aug. 10, 2004 133 Table_0133_ABC_1023_A.txt 12,043,351 Aug. 10, 2004 134 Table_0134_ABC_1023_B.txt 12,005,776 Aug. 10, 2004 135 Table_0135_ABC_1027_A.txt 12,225,822 Aug. 10, 2004 136 Table_0136_ABC_1027_B.txt 12,090,475 Aug. 10, 2004 137 Table_0137_ABC_1031_A.txt 12,144,420 Aug. 10, 2004 138 Table_0138_ABC_1031_B.txt 12,031,947 Aug. 10, 2004 139 Table_0139_ABC_1034_A.txt 12,086,509 Aug. 10, 2004 140 Table_0140_ABC_1034_B.txt 12,009,740 Aug. 10, 2004 141 Table_0141_ABC_1038_A.txt 12,141,976 Aug. 10, 2004 142 Table_0142_ABC_1038_B.txt 12,000,420 Aug. 10, 2004 143 Table_0143_ABC_1043_A.txt 12,139,378 Aug. 10, 2004 144 Table_0144_ABC_1043_B.txt 11,983,541 Aug. 10, 2004 145 Table_0145_ABC_1045_A.txt 11,979,407 Aug. 10, 2004 144 Table_0146_ABC_1045_B.txt 11,901,031 Aug. 10, 2004 147 Table_0147_ABC_1055_A.txt 12,018,874 Aug. 10, 2004 148 Table_0148_ABC_1055_B.txt 11,954,387 Aug. 10, 2004 149 Table_0149_ABC_1057_A.txt 12,062,420 Aug. 10, 2004 150 Table_0150_ABC_1057_B.txt 11,923,795 Aug. 10, 2004 151 Table_0151_ABC_1059_A.txt 12,037,304 Aug. 10, 2004 152 Table_0152_ABC_1059_B.txt 11,846,923 Aug. 10, 2004 153 Table_0153_ABC_1061_A.txt 12,162,153 Aug. 10, 2004 154 Table_0154_ABC_1061_B.txt 11,957,326 Aug. 10, 2004 155 Table_0155_ABC_1994_A.txt 12,063,327 Aug. 10, 2004 156 Table_0156_ABC_1994_B.txt 11,792,424 Aug. 10, 2004 157 Table_0157_ABC_2001_A.txt 12,144,366 Aug. 10, 2004 158 Table_0158_ABC_2001_B.txt 11,966,468 Aug. 10, 2004 159 Table_0159_BL_2032_A.txt 12,259,980 Aug. 12, 2004 160 Table_0160_BL_2032_B.txt 11,940,823 Aug. 12, 2004 161 Table_0161_BL_2033_A.txt 12,118,946 Aug. 13, 2004 162 Table_0462_BL_2033_B.txt 11,844,470 Aug. 13, 2004 163 Table_0163_BL_2035_A.txt 12,122,606 Aug. 13, 2004 164 Table_0164_BL_2035_B.txt 11,910,764 Aug. 13, 2004 Disc 4 of 22 165 Table_0165_BL_2036_A.txt 12,079,985 Aug. 13,2004 166 Table_0166_BL_2036_B.txt 11,804,136 Aug. 13,2004 167 Table_0167_BL_2037_A.txt 12,236,363 Aug. 13,2004 168 Table_0168_BL_2037_B.txt 12,114,941 Aug. 13,2004 169 Table_0169_BL_2038_A.txt 12,044,025 Aug. 13,2004 170 Table_0170_BL_2038_B.txt 11,887,911 Aug. 13,2004 171 Table_0171_BL_2082_A.txt 11,857,967 Aug. 13,2004 172 Table_0172_BL_2082_B.txt 11,730,035 Aug. 13,2004 173 Table_0173_BL_2083_A.txt 11,963,716 Aug. 13,2004 174 Table_0174_BL_2083_B.txt 11,796,670 Aug. 13 2004 175 Table_0175_BL_2086_A.txt 12,104,019 Aug. 13,2004 176 Table_0176_BL_2086_B.txt 11,965,947 Aug. 13,2004 177 Table_0177_BL_2088_A.txt 11,933,503 Aug. 13,2004 178 Table_0178_BL_2088_B.txt 11,706,861 Aug. 13,2004 179 Table_0179_BL_2090_A.txt 12,174,470 Aug. 13,2004 180 Table_0180_BL_2090_B.txt 12,102,048 Aug. 13,2004 181 Table_0181_BL_2091_A.txt 12,053,564 Aug. 13,2004 182 Table_0182_BL_2091_B.txt 12,003,445 Aug. 13,2004 183 Table_0183_BL_2097_A.txt 12,060,060 Aug. 13,2004 184 Table_0184_BL_2097_B.txt 11,779,676 Aug. 13,2004 185 Table_0185_BL_2099_A.txt 12,082,566 Aug. 13,2004 186 Table_0186_BL_2099_B.txt 11,894,123 Aug. 13,2004 187 Table_0187_BL_2100_A.txt 12,090,939 Aug. 13,2004 188 Table_0188_BL_2100_B.txt 11,854,774 Aug. 13,2004 189 Table_0189_BL_2101_A.txt 12,077,697 Aug. 13, 2004 190 Table_0190_BL_2101_B.txt 11,863,914 Aug. 13, 2004 191 Table_0191_BL_2103_A.txt 12,181,215 Aug. 13, 2004 192 Table_0192_BL_2103_B.txt 11,983,322 Aug. 13, 2004 193 Table_0193_BL_2125_A.txt 12,171,375 Aug. 13, 2004 194 Table_0194_BL_2125_B.txt 12,007,032 Aug. 13, 2004 195 Table_0195_BL_2126_A.txt 12,175,436 Aug. 13, 2004 196 Table_0196_BL_2126_B.txt 12,022,582 Aug. 13, 2004 197 Table_0197_BL_2127_A.txt 12,154,107 Aug. 13, 2004 198 Table_0198_BL_2127_B.txt 12,008,547 Aug. 13, 2004 199 Table_0199_BL_2128_A.txt 12,052,741 Aug. 13, 2004 200 Table_0200_BL_2128_B.txt 11,936,004 Aug. 13, 2004 201 Table_0201_BL_2129_A.txt 12,103,006 Aug. 13, 2004 202 Table_0202_BL_2129_B.txt 12,004,781 Aug. 13, 2004 203 Table_0203_BL_2267_A.txt 12,233,969 Aug. 13, 2004 204 Table_0204_BL_2267_B.txt 12,000,442 Aug. 13, 2004 205 Table_0205_BL_2268_A.txt 12,235,002 Aug. 13, 2004 206 Table_0206_BL_2268_B.txt 12,040,217 Aug. 13, 2004 207 Table_0207_BL_2269_A.txt 12,293,134 Aug. 13, 2004 208 Table_0208_BL_2269_B.txt 12,083,796 Aug. 13, 2004 209 Table_0209_BL_2271_A.txt 12,251,188 Aug. 13, 2004 210 Table_0210_BL_2271_B.txt 12,033,626 Aug. 13, 2004 211 Table_0211_CD1negMCL_1013_A.txt 12,069,354 Aug. 12, 2004 212 Table_0212_CD1negMCL_1013_B.txt 11,916,758 Aug. 12, 2004 213 Table_0213_CD1negMCL_1161_A.txt 12,063,571 Aug. 12, 2004 214 Table_0214_CD1negMCL_1116_B.txt 11,602,903 Aug. 12, 2004 215 Table_0215_CD1negMCL_1125_A.txt 11,980,433 Aug. 12, 2004 216 Table_0216_CD1negMCL_1125_B.txt 11,802,606 Aug. 12, 2004 217 Table_0217_CD1negMCL_1265_A.txt 12,073,913 Aug. 12, 2004 218 Table_0218_CD1negMCL_1265_B.txt 11,806,792 Aug. 12, 2004 219 Table_0219_CD1negMCL_2198_A.txt 12,205,777 Aug. 12, 2004 220 Table_0220_CD1negMCL_2198_B.txt 11,953,251 Aug. 12, 2004 Disc 5 of 22 221 Table_0221_CD1negMCL_2272_A.txt 12,140,519 Aug. 12, 2004 222 Table_0222_CD1negMCL_2272_B.txt 11,903,198 Aug. 12, 2004 223 Table_0223_CD1hegMCL_930_A.txt 12,085,551 Aug. 12, 2004 224 Table_0224_CD1negMCL_930_B.txt 12,029,054 Aug. 12, 2004 225 Table_0225_CD1negMCL_950_A.txt 12,048,303 Aug. 12, 2004 226 Table_0226_CD1negMCL_950_B.txt 11,966,572 Aug. 12, 2004 227 Table_0227_CD1negMCL_985_A.txt 12,152,037 Aug. 12, 2004 228 Table_0228_CD1negMCL_985_B.txt 11,987,764 Aug. 12, 2004 229 Table_0229_CD1negMCL_991_A.txt 11,970,755 Aug. 12, 2004 230 Table_0230_CD1negMCL_991_B.txt 11,906,922 Aug. 12, 2004 231 Table_0231_FH_2043_A.txt 12,107,383 Aug. 13, 2004 232 Table_0232_FH_2043_B.txt 11,759,404 Aug. 13, 2004 233 Table_0233_FH_2045_A.txt 12,018,780 Aug. 13, 2004 234 Table_0234_FH_2045_B.txt 11,760,937 Aug. 13, 2004 235 Table_0235_FH_2047_A.txt 12,196,386 Aug. 13, 2004 236 Table_0236_FH_2047_B.txt 12,038,844 Aug. 13, 2004 237 Table_0237_FH_2120_A.txt 12,057,634 Aug. 13, 2004 238 Table_0238_FH_2120_B.txt 11,899,644 Aug. 13, 2004 239 Table_0239_FH_2123_A.txt 12,102,087 Aug. 13, 2004 240 Table_0240_FH_2123_B.txt 11,974,939 Aug. 13, 2004 241 Table_0241_FH_2124_A.txt 11,981,409 Aug. 13, 2004 242 Table_0242_FH_2124_B.txt 11,728,703 Aug. 13, 2004 243 Table_0243_FH_2138_A.txt 12,246,913 Aug. 13, 2004 244 Table_0244_FH_2138_B.txt 12,113,245 Aug. 13, 2004 245 Table_0245_FH_2139_A.txt 12,131,403 Aug. 13, 2004 248 Table_0246_FH_2139_B.txt 12,113,768 Aug. 13, 2004 247 Table_0247_FH_2140_A.txt 12,195,197 Aug. 13, 2004 248 Table_0248_FH_2140_B.txt 12,119,738 Aug. 13, 2004 249 Table_0249_FH_2141_A.txt 12,208,402 Aug. 13, 2004 250 Table_0250_FH_2141_B.txt 12,147,002 Aug. 13, 2004 251 Table_0251_FH_2142_A.txt 12,219,828 Aug. 12, 2004 252 Table_0252_FH_2142_B.txt 12,085,775 Aug. 13, 2004 253 Table_0253_FH_2143_A.txt 12,006,329 Aug. 13, 2004 254 Table_0254_FH_2143_B.txt 11,935,243 Aug. 13, 2004 255 Table_0255_FH_2159_A.txt 12,188,960 Aug. 13, 2004 256 Table_0256_FH_2159_B.txt 11,879,679 Aug. 12, 2004 257 Table_0257_FH_2160_A.txt 12,192,094 Aug. 13, 2004 258 Table_0258_FH_2160_B.txt 11,931,687 Aug. 13, 2004 259 Table_0259_FH_2161_A.txt 12,253,214 Aug. 13, 2004 260 Table_0260_FH_2161_B.txt 12,051,739 Aug. 16, 2004 261 Table_0261_FH_2162_A.txt 12,191,210 Aug. 16, 2004 262 Table_0262_FH_2162_B.txt 11,952,602 Aug. 13, 2004 263 Table_0263_FH_2164_A.txt 12,272,173 Aug. 13, 2004 264 Table_0264_FH_2164_B.txt 12,019,060 Aug. 13, 2004 265 Table_0265_FH_2167_A.txt 12,337,573 Aug. 13, 2004 266 Table_0266_FH_2167_B.txt 12,107,528 Aug. 16, 2004 267 Table_0267_FL_1073_A.txt 12,249,411 Aug. 13, 2004 268 Table_0268_FL_1073_B.txt 12,008,104 Aug. 16, 2004 269 Table_0269_FL_1074_A.txt 12,197,601 Aug. 13, 2004 270 Table_0270_FL_1074_B.txt 11,933,006 Aug. 16, 2004 271 Table_0271_FL_1075_A.txt 12,222,781 Aug. 16, 2004 272 Table_0272_FL_1075_B.txt 12,070,708 Aug. 13, 2004 273 Table_0273_FL_1076_A.txt 12,270,162 Aug. 16, 2004 274 Table_0274_FL_1076_B.txt 12,076,340 Aug. 16, 2004 275 Table_0275_FL_1077_A.txt 12,180,432 Aug. 16, 2004 276 Table_0276_FL_1077_B.txt 11,836,673 Aug. 13, 2004 Disc 6 of 22 277 Table_0277_FL_1078_A.txt 12,240,701 Aug. 13, 2004 278 Table_0278_FL_1078_B.txt 11,975,063 Aug. 13, 2004 279 Table_0279_FL_1080_A.txt 12,227,672 Aug. 13, 2004 280 Table_0280_FL_1080_B.txt 12,004,087 Aug. 16, 2004 281 Table_0281_FL_1081_A.txt 12,081,406 Aug. 13, 2004 282 Table_0282_FL_1081_B.txt 11,942,019 Aug. 16, 2004 283 Table_0283_FL_1083_A.txt 12,197,692 Aug. 13, 2004 284 Table_0284_FL_1083_B.txt 12,000,356 Aug. 13, 2004 285 Table_0285_FL_1085_A.txt 12,198,722 Aug. 13, 2004 286 Table_0286_FL_1085_B.txt 11,966,361 Aug. 16, 2004 287 Table_0287_FL_1086_A.txt 12,202,810 Aug. 13, 2004 288 Table_0288_FL_1086_B.txt 12,059,339 Aug. 16, 2004 289 Table_0289_FL_735_A.txt 12,093,811 Aug. 15, 2004 290 Table_0290_FL_735_B.txt 11,986,038 Aug. 13, 2004 291 Table_0291_FL_738_A.txt 12,150,187 Aug. 16, 2004 292 Table_0292_FL_738_B.txt 11,937,614 Aug. 13, 2004 293 Table_0293_FL_739_A.txt 12,166,069 Aug. 16, 2004 294 Table_0294_FL_739_B.txt 11,924,390 Aug. 13, 2004 295 Table_0295_FL_878_A.txt 12,199,551 Aug. 16, 2004 296 Table_0296_FL_878_B.txt 11,965,688 Aug. 16, 2004 297 Table_0297_FL_879_A.txt 12,153,527 Aug. 16, 2004 298 Table_0298_FL_879_B.txt 11,953,408 Aug. 13, 2004 299 Table_0299_FL_886_A.txt 12,167,246 Aug. 13, 2004 300 Table_0300_FL_886_B.txt 11,837,538 Aug. 16, 2004 301 Table_0301_FL_888_A.txt 12,069,433 Aug. 16, 2004 302 Table_0302_FL_888_B.txt 1,768,475 Aug. 16, 2004 303 Table_0303_FL_1087_A.txt 12,234,602 Aug. 16, 2004 304 Table_0304_FL_1087_B.txt 12,029,081 Aug. 16, 2004 305 Table_0305_FL_1088_A.txt 12,176,960 Aug. 12, 2004 306 Table_0306_FL_1088_B.txt 11,929,385 Aug. 13, 2004 307 Table_0307_FL_1089_A.txt 12,235,001 Aug. 16, 2004 308 Table_0308_FL_1089_B.txt 12,002,707 Aug. 16, 2004 309 Table_0309_FL_1090_A.txt 12,187,091 Aug. 13, 2004 310 Table_0310_FL_1090_B.txt 11,930,228 Aug. 13, 2004 311 Table_0311_FL_1097_A.txt 12,035,807 Aug. 12, 2004 312 Table_0312_FL_1097_B.txt 11,998,469 Aug. 12, 2004 313 Table_0313_FL_1098_A.txt 12,163,535 Aug. 13, 2004 314 Table_0314_FL_1098_B.txt 11,910,993 Aug. 12, 2004 315 Table_0315_FL_1099_A.txt 12,140,701 Aug. 16, 2004 316 Table_0316_FL_1099_B.txt 11,963,778 Aug. 12, 2004 317 Table_0317_FL_1102_A.txt 12,041,808 Aug. 13, 2004 318 Table_0318_FL_1102_B.txt 11,873,396 Aug. 13, 2004 319 Table_0319_FL_1104_A.txt 12,147,511 Aug. 16, 2004 320 Table_0320_FL_1104_B.txt 11,972,622 Aug. 12, 2004 321 Table_0321_FL_1106_A.txt 12,074,579 Aug. 13, 2004 322 Table_0322_FL_1106_B.txt 11,991,893 Aug. 16, 2004 323 Table_0323_FL_1107_A.txt 12,022,734 Aug. 13, 2004 324 Table_0324_FL_1107_B.txt 11,887,794 Aug. 16, 2004 325 Table_0325_FL_1183_A.txt 12,062,144 Aug. 16, 2004 326 Table_0326_FL_1183_B.txt 11,831,515 Aug. 16, 2004 327 Table_0327_FL_1184_A.txt 12,095,457 Aug. 13, 2004 328 Table_0328_FL_1184_B.txt 11,978,023 Aug. 16, 2004 329 Table_0329_FL_1185_A.txt 11,929,326 Aug. 13, 2004 330 Table_0330_FL_1185_B.txt 11,784,070 Aug. 12, 2004 331 Table_0331_FL_1186_A.txt 11,962,956 Aug. 16, 2004 332 Table_0332_FL_1186_B.txt 11,896,127 Aug. 12, 2004 Disc 7 of 22 333 Table_0333_FL_1416_A.txt 12,185,087 Aug. 16, 2004 334 Table_0334_FL_1416_B.txt 12,073,389 Aug. 12, 2004 335 Table_0335_FL_1417_A.txt 12,131,418 Aug. 13, 2004 336 Table_0336_FL_1417_B.txt 12,019,667 Aug. 13, 2004 337 Table_0337_FL_1418_A.txt 12,063,921 Aug. 13, 2004 338 Table_0338_FL_1418_B.txt 11,790,370 Aug. 16, 2004 339 Table_0339_FL_1419_A.txt 12,182,797 Aug. 16, 2004 340 Table_0340_FL_1419_B.txt 12,070,844 Aug. 16, 2004 341 Table_0341_FL_1422_A.txt 12,008,099 Aug. 13, 2004 342 Table_0342_FL_1422_B.txt 11,933,820 Aug. 13, 2004 343 Table_0343_FL_1425_A.txt 12,144,306 Aug. 16, 2004 344 Table_0344_FL_1425_B.txt 12,018,656 Aug. 16, 2004 345 Table_0345_FL_1426_A.txt 12,156,576 Aug. 12, 2004 346 Table_0346_FL_1426_B.txt 12,009,428 Aug. 16, 2004 347 Table_0347_FL_1427_A.txt 11,984,418 Aug. 13, 2004 348 Table_0348_FL_1427_B.txt 11,739,570 Aug. 16, 2004 349 Table_0349_FL_1428_A.txt 12,158,088 Aug. 16, 2004 350 Table_0350_FL_1428_B;txt 11,971,322 Aug. 13, 2004 351 Table_0351_FL_1429_A.txt 11,886,842 Aug. 13, 2004 352 Table_0352_FL_1429_B.txt 11,785,560 Aug. 13, 2004 353 Table_0353_FL_1432_A.txt 11,936,050 Aug. 13, 2004 354 Table_0354_FL_1432_B.txt 11,718,155 Aug. 16, 2004 355 Table_0355_FL_1436_A.txt 12,095,379 Aug. 16, 2004 356 Table_0356_FL_1436_B.txt 11,970,436 Aug. 13, 2004 357 Table_0357_FL_1440_A.txt 12,133,947 Aug. 16, 2004 358 Table_0358_FL_1440_B.txt 11,797,142 Aug. 16, 2004 359 Table_0359_FL_1445_A.txt 12,110,202 Aug. 16, 2004 360 Table_0360_FL_1445_B.txt 11,848,178 Aug. 16, 2004 361 Table_0361_FL_1450_A.txt 12,106,422 Aug. 16, 2004 362 Table_0362_FL_1450_B.txt 11,943,968 Aug. 12, 2004 363 Table_0363_FL_1472_A.txt 12,212,076 Aug. 13, 2004 364 Table_0364_FL_1472_B.txt 12,036,719 Aug. 16, 2004 365 Table_0365_FL_1473_A.txt 12,221,319 Aug. 13, 2004 366 Table_0366_FL_1473_B.txt 12,071,897 Aug. 13, 2004 367 Table_0367_FL_1474_A.txt 12,150,017 Aug. 12, 2004 368 Table_0368_FL_1474_B.txt 11,998,046 Aug. 16, 2004 369 Table_0369_FL_1476_A.txt 12,125,076 Aug. 16, 2004 370 Table_0370_FL_1476_B.txt 11,895,072 Aug. 13, 2004 371 Table_0371_FL_1477_A.txt 12,017,848 Aug. 16, 2004 372 Table_0372_FL_1477_B.txt 11,864,262 Aug. 16, 2004 373 Table_0373_FL_1478_A.txt 11,952,576 Aug. 13, 2004 374 Table_0374_FL_1478_B.txt 11,803,675 Aug. 16, 2004 375 Table_0375_FL_1479_A.txt 12,010,188 Aug. 13, 2004 376 Table_0376_FL_1479_B.txt 11,818,187 Aug. 16, 2004 377 Table_0377_FL_1480_A.txt 11,988,630 Aug. 13, 2004 378 Table_0378_FL_1480_B.txt 11,730,265 Aug. 13, 2004 379 Table_0379_FL_1579_A.txt 12,081,097 Aug. 13, 2004 380 Table_0380_FL_1579_B.txt 11,905,139 Aug. 13, 2004 381 Table_0381_FL_1580_A.txt 12,135,862 Aug. 12, 2004 382 Table_0382_FL_1580_B.txt 11,949,530 Aug. 16, 2004 383 Table_0383_FL_1581_A.txt 12,108,683 Aug. 13, 2004 384 Table_0384_FL_1581_B.txt 1,883,016 Aug. 13, 2004 385 Table_0385_FL_1582_A.txt 12,176,104 Aug. 13, 2004 386 Table_0386_FL_1582_B.txt 11,970,276 Aug. 16, 2004 387 Table_0387_FL_1583_A.txt 12,078,610 Aug. 16, 2004 388 Table_0388_FL_1583_B.txt 11,871,179 Aug. 13, 2004 Disc 8 of 22 389 Table_0389_FL_1584_A.txt 12,169,682 Aug. 13, 2004 390 Table_0390_FL_1584_B.txt 12,035,358 Aug. 16, 2004 391 Table_0391_FL_1585_A.txt 12,228,755 Aug. 13, 2004 392 Table_0392_FL_1585_B.txt 11,995,646 Aug. 13, 2004 393 Table_0393_FL_1586_A.txt 12,133,764 Aug. 16, 2004 394 Table_0394_FL_1586_B.txt 11,897,600 Aug. 16, 2004 395 Table_0395_FL_1588_A.txt 11,934,635 Aug. 12, 2004 396 Table_0396_FL_1588_B.txt 11,772,260 Aug. 13, 2004 397 Table_0397_FL_1589_A.txt 12,139,333 Aug. 13, 2004 398 Table_0398_FL_1589_B.txt 12,016,431 Aug. 13, 2004 399 Table_0399_FL_1591_A.txt 12,007,646 Aug. 13, 2004 400 Table_0400_FL_1591_B.txt 12,031,102 Aug. 13, 2004 401 Table_0401_FL_1594_A.txt 11,931,355 Aug. 16, 2004 402 Table_0402_FL_1594_B.txt 12,009,670 Aug. 13, 2004 403 Table_0403_FL_1595_A.txt 11,824,656 Aug. 16, 2004 404 Table_0404_FL_1595_B.txt 11,867,729 Aug. 13, 2004 405 Table_0405_FL_1598_A.txt 12,136,878 Aug. 13, 2004 406 Table_0406_FL_1598_B.txt 12,067,658 Aug. 16, 2004 407 Table_0407_FL_1599_A.txt 12,029,574 Aug. 16, 2004 408 Table_0408_FL_1599_B.txt 11,820,074 Aug. 16, 2004 409 Table_0409_FL_1603_A.txt 12,091,394 Aug. 13, 2004 410 Table_0410_FL_1603_B.txt 11,869,687 Aug. 12, 2004 411 Table_0411_FL_1604_A.txt 12,027,557 Aug. 16, 2004 412 Table_0412_FL_1604_B.txt 11,817,875 Aug. 13, 2004 413 Table_0413_FL_1606_A.txt 12,068,506 Aug. 13, 2004 414 Table_0414_FL_1606_B.txt 11,902,253 Aug. 13, 2004 415 Table_0415_FL_1607_A.txt 12,088,022 Aug. 13, 2004 416 Table_0416_FL_1607_B.txt 11,942,795 Aug. 16, 2004 417 Table_0417_FL_1608_A.txt 12,051,428 Aug. 16, 2004 418 Table_0418_FL_1608_B.txt 11,758,034 Aug. 16, 2004 419 Table_0419_FL_1610_A.txt 12,164,325 Aug. 13, 2004 420 Table_0420_FL_1610_B.txt 11,884,041 Aug. 16, 2004 421 Table_0421_FL_1611_A.txt 12,052,511 Aug. 16, 2004 422 Table_0422_FL_1611_B.txt 11,821,581 Aug. 13, 2004 423 Table_0423_FL_1616_A.txt 12,183,189 Aug. 16, 2004 424 Table_0424_FL_1616_B.txt 11,967,672 Aug. 12, 2004 425 Table_0425_FL_1617_A.txt 12,059,647 Aug. 13, 2004 426 Table_0426_FL_1617_B.txt 11,857,787 Aug. 13, 2004 427 Table_0427_FL_1619_A.txt 12,145,748 Aug. 13, 2004 428 Table_0428_FL_1619_B.txt 12,010,810 Aug. 16, 2004 429 Table_0429_FL_1620_A.txt 12,151,309 Aug. 16, 2004 430 Table_0430_FL_1620_B.txt 11,970,646 Aug. 13, 2004 431 Table_0431_FL_1622_A.txt 12,153,195 Aug. 13, 2004 432 Table_0432_FL_1622_B.txt 12,003,231 Aug. 13, 2004 433 Table_0433_FL_1623_A.txt 12,252,791 Aug. 16, 2004 434 Table_0434_FL_1623_B.txt 12,120,904 Aug. 16, 2004 435 Table_0435_FL_1624_A.txt 12,175,743 Aug. 13, 2004 436 Table_0436_FL_1624_B.txt 12,146,244 Aug. 13, 2004 437 Table_0437_FL_1625_A.txt 12,212,124 Aug. 13, 2004 438 Table_0438_FL_1625_B.txt 12,042,056 Aug. 13, 2004 439 Table_0439_FL_1626_A.txt 12,180,263 Aug. 13, 2004 440 Table_0440_FL_1626_B.txt 12,013,477 Aug. 16, 2004 441 Table_0441_FL_1627_A.txt 12,168,379 Aug. 13, 2004 442 Table_0442_FL_1627_B.txt 12,025,929 Aug. 13, 2004 443 Table_0443_FL_1628_A.txt 12,164,556 Aug. 13, 2004 444 Table_0444_FL_1628_B.txt 11,605,629 Aug. 13, 2004 Disc 9 of 22 445 Table_0445_FL_1637_A.txt 12,059,851 Aug. 13, 2004 446 Table_0446_FL_1637_B.txt 11,851,760 Aug. 16, 2004 447 Table_0447_FL_1638_A.txt 12,155,584 Aug. 12, 2004 448 Table_0448_FL_1638_B.txt 11,953,771 Aug. 13, 2004 449 Table_0449_FL_1639_A.txt 12,104,432 Aug. 13, 2004 450 Table_0450_FL_1639_B.txt 11,885,752 Aug. 16, 2004 451 Table_0451_FL_1643_A.txt 12,113,590 Aug. 16, 2004 452 Table_0452_FL_1643_B.txt 11,976,289 Aug. 16, 2004 453 Table_0453_FL_1644_A.txt 12,135,052 Aug. 16, 2004 454 Table_0454_FL_1644_B.txt 11,864,285 Aug. 13, 2004 455 Table_0455_FL_1645_A.txt 12,082,408 Aug. 13, 2004 456 Table_0456_FL_1645_B.txt 11,794,548 Aug. 16, 2004 457 Table_0457_FL_1646_A.txt 12,205,460 Aug. 16, 2004 458 Table_0458_FL_1646_B.txt 12,063,381 Aug. 13, 2004 459 Table_0459_FL_1647_A.txt 12,014,730 Aug. 16, 2004 460 Table_0460_FL_1647_B.txt 11,815,426 Aug. 13, 2004 461 Table_0461_FL_1648_A.txt 12,129,890 Aug. 13, 2004 462 Table_0462_FL_1648_B.txt 11,970,207 Aug. 13, 2004 463 Table_0463_FL_1652_A.txt 12,094,102 Aug. 16, 2004 464 Table_0464_FL_1652_B.txt 11,858,393 Aug. 12, 2004 465 Table_0465_FL_1654_A.txt 12,054,434 Aug. 16, 2004 466 Table_0466_FL_1654_B.txt 11,835,533 Aug. 13, 2004 467 Table_0467_FL_1655_A.txt 12,011,007 Aug. 12, 2004 468 Table_0468_FL_1655_B.txt 11,839,935 Aug. 12, 2004 469 Table_0469_FL_1656_A.txt 11,998,067 Aug. 16, 2004 470 Table_0470_FL_1656_B.txt 11,783,615 Aug. 13, 2004 471 Table_0471_FL_1657_A.txt 12,095,831 Aug. 13, 2004 472 Table_0472_FL_1657_B.txt 11,872,675 Aug. 12, 2004 473 Table_0473_FL_1660_A.txt 11,984,440 Aug. 13, 2004 474 Table_0474_FL_1660_B.txt 11,793,957 Aug. 16, 2004 475 Table_0475_FL_1661_A.txt 12,115,368 Aug. 13, 2004 476 Table_0476_FL_1661_B.txt 11,907,640 Aug. 16, 2004 477 Table_0477_FL_1662_A.txt 11,935,841 Aug. 13, 2004 478 Table_0478_FL_1662_B.txt 11,655,368 Aug. 16, 2004 479 Table_0479_FL_1664_A.txt 12,057,315 Aug. 13, 2004 480 Table_0480_FL_1664_B.txt 11,881,016 Aug. 13, 2004 481 Table_0481_FL_1669_A.txt 12,067,483 Aug. 16, 2004 482 Table_0482_FL_1669_B.txt 11,942,861 Aug. 13, 2004 483 Table_0483_FL_1670_A.txt 12,037,021 Aug. 16, 2004 484 Table_0484_FL_1670_B.txt 11,862,504 Aug. 13, 2004 485 Table_0485_FL_1675_A.txt 12,062,632 Aug. 16, 2004 486 Table_0486_FL_1675_B.txt 11,823,358 Aug. 13, 2004 487 Table_0487_FL_1681_A.txt 11,978,013 Aug. 13, 2004 488 Table_0488_FL_1681_B.txt 11,745,731 Aug. 16, 2004 489 Table_0489_FL_1883_A.txt 11,919,352 Aug. 12, 2004 490 Table_0490_FL_1683_B.txt 11,780,543 Aug. 12, 2004 491 Table_0491_FL_1684_A.txt 12,186,541 Aug. 13, 2004 492 Table_0492_FL_1684_B.txt 12,056,872 Aug. 13, 2004 493 Table_0493_FL_1716_A.txt 12,160,373 Aug. 16, 2004 494 Table_0494_FL_1716_B.txt 11,995,492 Aug. 13, 2004 495 Table_0495_FL_1717_A.txt 11,938,675 Aug. 13, 2004 496 Table_0496_FL_1717_B.txt 11,950,078 Aug. 12, 2004 497 Table_0497_FL_1718_A.txt 12,102,928 Aug. 12, 2004 498 Table_0498_FL_1718_B.txt 1,946,324 Aug. 12, 2004 499 Table_0499_FL_1719_A.txt 11,980,431 Aug. 16, 2004 500 Table_0500_FL_1719_B.txt 11,941,393 Aug. 13, 2004 501 Table_0501_FL_1720_A.txt 11,957,606 Aug. 31, 2004 502 Table_0502_FL_1720_B.txt 11,790,878 Aug. 31, 2004 503 Table_0503_FL_1729_A.txt 12,015,501 Aug. 31, 2004 504 Table_0504_FL_1729_B.txt 12,003,112 Aug. 31, 2004 Disc 10 of 22 505 Table_0505_FL_1732_A.txt 12,019,768 Aug. 31, 2004 506 Table_0506_FL_1732_B.txt 11,937,384 Aug. 31, 2004 507 Table_0507_FL_1735_A.txt 12,229,281 Aug. 31, 2004 508 Table_0508_FL_1735_B.txt 12,010,813 Aug. 31, 2004 509 Table_0509_FL_1761_A.txt 12,013,113 Aug. 31, 2004 510 Table_0510_FL_1761_B.txt 11,84S,381 Aug. 31, 2004 511 Table_0511_FL_1764_A.txt 12,072,874 Aug. 31, 2004 512 Table_0512_FL_1764_B.txt 12,067,878 Aug. 31, 2004 513 Table_0513_FL_1768_A.txt 12,164,392 Aug. 31, 2004 514 Table_0514_FL_1768_B.txt 12,159,717 Aug. 31, 2004 515 Table_0515_FL_1771_A.txt 12,171,432 Aug. 31, 2004 516 Table_0516_FL_1771_B.txt 11,981,937 Aug. 31, 2004 517 Table_0517_FL_1772_A.txt 12,099,592 Aug. 31, 2004 518 Table_0518_FL_1772_B.txt 12,044,119 Aug. 31, 2004 519 Table_0519_FL_1788_A.txt 12,057,485 Aug. 31, 2004 520 Table_0520_FL_1788_B.txt 12,000,622 Aug. 31, 2004 521 Table_0521_FL_1790_A.txt 11,866,178 Aug. 13, 2004 522 Table_0522_FL_1790_B.txt 11,879,605 Aug. 16, 2004 523 Table_0523_FL_1792_A.txt 12,107,831 Aug. 12, 2004 524 Table_0524_FL_1792_B.txt 11,981,731 Aug. 16, 2004 525 Table_0525_FL_1795_A.txt 12,147,382 Aug. 13, 2004 526 Table_0526_FL_1795_B.txt 12,027,851 Aug. 13, 2004 527 Table_0527_FL_1797_A.txt 12,007,290 Aug. 16, 2004 528 Table_0528_FL_1797_B.txt 11,977,859 Aug. 13, 2004 529 Table_0529_FL_1799_A.txt 12,136,510 Aug. 13, 2004 530 Table_0530_FL_1799_B.txt 12,019,206 Aug. 16, 2004 531 Table_0531_FL_1810_A.txt 12,086,662 Aug. 13, 2004 532 Table_0532_FL_1810_B.txt 11,977,958 Aug. 16, 2004 533 Table_0533_FL_1811_A.txt 12,122,670 Aug. 16, 2004 534 Table_0534_FL_1811_B.txt 11,929,935 Aug. 13, 2004 535 Table_0535_FL_1825_A.txt 11,967,784 Aug. 13, 2004 536 Table_0536_FL_1825_B.txt 11,932,434 Aug. 12, 2004 537 Table_0537_FL_1827_A.txt 11,955,058 Aug. 13, 2004 538 Table_0538_FL_1827_B.txt 11,969,116 Aug. 13, 2004 539 Table_0539_FL_1828_A.txt 12,029,099 Aug. 12, 2004 540 Table_0540_FL_1828_B.txt 11,877,005 Aug. 13, 2004 541 Table_0541_FL_1829_A.txt 12,014,127 Aug. 13, 2004 542 Table_0542_FL_1829_B.txt 11,885,190 Aug. 12, 2004 543 Table_0543_FL_1830_A.txt 12,089,030 Aug. 13, 2004 544 Table_0544_FL_1830_B.txt 11,935,691 Aug. 13, 2004 545 Table_0545_FL_1833_A.txt 12,162,920 Aug. 16, 2004 546 Table_0546_FL_1833_B.txt 11,990,591 Aug. 13, 2004 547 Table_0547_FL_1834_A.txt 11,952,185 Aug. 13, 2004 548 Table_0548_FL_1834_B.txt 11,906,810 Aug. 12, 2004 549 Table_0549_FL_1835_A.txt 12,122,553 Aug. 13, 2004 559 Table_0550_FL_1835_B.txt 12,017,691 Aug. 12, 2004 551 Table_0551_FL_1836_A.txt 12,187,532 Aug. 13, 2004 552 Table_0552_FL_1836_B.txt 12,063,526 Aug. 16, 2004 553 Table_0553_FL_1837_A.txt 12,044,450 Aug. 16, 2004 554 Table_0554_FL_1837_B.txt 1,945,547 Aug. 16, 2004 555 Table_0555_FL_1038_A.txt 12,024,726 Aug. 13, 2004 556 Table_0556_FL_1838_B.txt 11,908,269 Aug. 16, 2004 557 Table_0557_FL_1839_A.txt 12,063,812 Aug. 16, 2004 558 Table_0558_FL_1839_B.txt 11,957,591 Aug. 13, 2004 559 Table_0559_FL_1841_A.txt 12,137,503 Aug. 13, 2004 560 Table_0560_FL_1841_B.txt 11,977,376 Aug. 13, 2004 Disc 11 of 22 561 Table_0561_FL_1842_A.txt 11,838,323 Aug. 13, 2004 562 Table_0562_FL_1042_B.txt 11,777,270 Aug. 13, 2004 563 Table_0563_FL_1844_A.txt 12,101,060 Aug. 13, 2004 564 Table_0564_FL_1844_B.txt 11,948,925 Aug. 12, 2004 565 Table_0565_FL_1845_A.txt 12,080,776 Aug. 13, 2004 566 Table_0566_FL_1845_B.txt 11,896,386 Aug. 12, 2004 567 Table_0567_FL_1846_A.txt 12,068,884 Aug. 13, 2004 568 Table_0568_FL_1846_B.txt 11,927,246 Aug. 16, 2004 569 Table_0569_FL_1848_A.txt 12,203,436 Aug. 13, 2004 570 Table_0570_FL_1848_B.txt 12,066,922 Aug. 16, 2004 571 Table_0571_FL_1850_A.txt 12,150,601 Aug. 16, 2004 572 Table_0572_FL_1850_B.txt 12,041,327 Aug. 13, 2004 573 Table_0573_FL_1851_A.txt 12,182,233 Aug. 12, 2004 574 Table_0574_FL_1851_B.txt 12,075,493 Aug. 16, 2004 575 Table_0575_FL_1853_A.txt 12,087,688 Aug. 13, 2004 576 Table_0576_FL_1853_B.txt 11,933,285 Aug. 16, 2004 577 Table_0577_FL_1854_A.txt 12,155,291 Aug. 12, 2004 578 Table_0578_FL_1854_B.txt 11,959,793 Aug. 16, 2004 579 Table_0579_FL_1855_A.txt 12,238,359 Aug. 16, 2004 580 Table_0580_FL_1855_B.txt 12,034,894 Aug. 12, 2004 581 Table_0581_FL_1857_A.txt 12,205,129 Aug. 16, 2004 582 Table_0582_FL_1857_B.txt 12,076,155 Aug. 16, 2004 583 Table_0583_FL_1861_A.txt 12,040,917 Aug. 16, 2004 584 Table_0584_FL_1861_B.txt 11,858,142 Aug. 13, 2004 585 Table_0585_FL_1862_A.txt 12,041,052 Aug. 13, 2004 585 Table_0586_FL_1862_B.txt 11,924,463 Aug. 13, 2004 587 Table_0587_FL_1863_A.txt 12,058,282 Aug. 13, 2004 588 Table_0588_FL_1863_B.txt 11,905,465 Aug. 12, 2004 589 Table_0589_FL_1864_A.txt 12,114,627 Aug. 16, 2004 590 Table_0590_FL_1864_B.txt 11,910,825 Aug. 13, 2004 591 Table_0591_FL_1866_A.txt 12,192,098 Aug. 12, 2004 592 Table_0592_FL_1866_B.txt 12,052,374 Aug. 13, 2004 593 Table_0593_FL_1870_A.txt 12,017,057 Aug. 13, 2004 594 Table_0594_FL_1870_B.txt 11,959,328 Aug. 16, 2004 595 Table_0595_FL_1873_A.txt 11,193,964 Aug. 12, 2004 596 Table_0596_FL_1873_B.txt 12,071,638 Aug. 13, 2004 597 Table_0597_FL_1874_A.txt 12,096,385 Aug. 13, 2004 598 Table_0598_FL_1874_B.txt 12,004,410 Aug. 16, 2004 599 Table_0599_FL_1876_A.txt 12,130,466 Aug. 13, 2004 600 Table_0600_FL_1876_B.txt 11,894,443 Aug. 13, 2004 601 Table_0601_FL_1879_A.txt 12,047,598 Aug. 16, 2004 602 Table_0602_FL_1879_B.txt 11,816,302 Aug. 16, 2004 603 Table_0603_FL_1880_A.txt 11,925,145 Aug. 16, 2004 604 Table_0604_FL_1880_B.txt 11,872,612 Aug. 12, 2004 605 Table_0605_FL_1882_A.txt 12,006,192 Aug. 13, 2004 696 Table_0606_FL_1882_B.txt 11,755,896 Aug. 13, 2004 607 Table_0607_FL_1884_A.txt 11,983,446 Aug. 13, 2004 608 Table_0608_FL_1884_B.txt 11,850,008 Aug. 12, 2004 609 Table_0609_FL_1885_A.txt 12,000,301 Aug. 13, 2004 610 Table_0610_FL_1885_B.txt 11,802,742 Aug. 16, 2004 611 Table_0611_FL_1887_A.txt 12,008,565 Aug. 13, 2004 612 Table_0612_FL_1887_B.txt 11,916,475 Aug. 16, 2004 613 Table_0613_FL_1888_A.txt 12,027,927 Aug. 13, 2004 614 Table_0614_FL_1888_B.txt 11,850,399 Aug. 13, 2004 615 Table_0615_FL_1890_A.txt 11,991,887 Aug. 13, 2004 616 Table_0616_FL_1890_B.txt 11,723,176 Aug. 16, 2004 617 Table_0617_FL_1894_A.txt 11,970,378 Aug. 12, 2004 618 Table_0618_FL_1894_B.txt 11,814,812 Aug. 16, 2004 619 Table_0619_FL_1896_A.txt 11,986,044 Aug. 13, 2004 620 Table_0620_FL_1896_B.txt 11,846,067 Aug. 16, 2004 621 Table_0621_FL_1897_A.txt 11,936,008 Aug. 13, 2004 Disc 12 of 22 622 Table_0622_FL_1897_B.txt 11,766,595 Aug. 16, 2004 623 Table_0623_FL_1898_A.txt 12,044,053 Aug. 16, 2004 624 Table_0624_FL_1898_B.txt 11,886,226 Aug. 13, 2004 625 Table_0625_FL_1900_A.txt 11,950,630 Aug. 13, 2004 626 Table_0626_FL_1900_B.txt 11,714,418 Aug. 13, 2004 627 Table_0627_FL_1903_A.txt 12,114,153 Aug. 13, 2004 628 Table_0628_FL_1903_B.txt 11,971,160 Aug. 13, 2004 629 Table_0629_FL_1904_A.txt 12,005,503 Aug. 12, 2004 630 Table_0630_FL_1904_B.txt 11,844,302 Aug. 13, 2004 631 Table_0631_FL_1905_A.txt 12,001,380 Aug. 13, 2004 632 Table_0632_FL_1905_B.txt 11,761,255 Aug. 16, 2004 633 Table_0633_FL_1906_A.txt 11,942,117 Aug. 13, 2904 634 Table_0634_FL_1906_B.txt 11,733,601 Aug. 13, 2004 635 Table_0635_FL_1907_A.txt 11,966,185 Aug. 16, 2004 636 Table_0636_FL_1907_B.txt 11,824,403 Aug. 16, 2004 637 Table_0637_FL_1910_A.txt 12,086,740 Aug. 16, 2004 638 Table_0638_FL_1910_B.txt 11,975,275 Aug. 13, 2004 639 Table_0639_FL_1912_A.txt 12,103,924 Aug. 13, 2004 640 Table_0640_FL_1912_B.txt 11,961,340 Aug. 13, 2004 641 Table_0641_FL_1913_A.txt 12,113,471 Aug. 12, 2004 642 Table_0642_FL_1913_B.txt 11,876,693 Aug. 13, Z004 643 Table_0643_FL_1916_A.txt 12,083,419 Aug. 12, 2004 644 Table_0644_FL_1916_B.txt 11,867,823 Aug. 16, 2004 645 Table_0645_FL_1918_A.txt 12,100,679 Aug. 13, 2004 646 Table_0646_FL_1918_B.txt 11,752,183 Aug. 16, 2004 647 Table_0647_FL_1919_A.txt 11,911,303 Aug. 13, 2004 648 Table_0648_FL_1919_B.txt 11,746,064 Aug. 16, 2004 649 Table_0649_GCB_412_A.txt 12,159,887 Aug. 16, 2004 650 Table_0650_GCB_412_B.txt 11,936,122 Aug. 16, 2004 651 Table_0651_GCB_415_A.txt 12,127,395 Aug. 13, 2004 652 Table_0652_GCB_415_B.txt 11,974,456 Aug. 13, 2004 663 Table_0653_GCB_421_A.txt 12,089,656 Aug. 12, 2004 654 Table_0654_GCB_421_B.txt 11,977,458 Aug. 13, 2004 655 Table_0655_GCB_424_A.txt 12,234,103 Aug. 12, 2004 656 Table_0656_GCB_424_B.txt 11,982,465 Aug. 12, 2004 657 Table_0657_GCB_433_A.txt 12,281,184 Aug. 16, 2004 658 Table_0658_GCB_433_B.txt 12,083,431 Aug. 13, 2004 659 Table_0659_GCB_434_A.txt 12,293,242 Aug. 13, 2004 660 Table_0660_GCB_434_B.txt 12,107,279 Aug. 16, 2004 661 Table_0661_GCB_438_A.txt 12,221,161 Aug. 16, 2004 662 Table_0662_GCB_438_B.txt 12,019,388 Aug. 13, 2004 663 Table_0663_GCB_439_A.txt 11,992,537 Aug. 13, 2004 664 Table_0664_GCB_439_B.txt 11,899,300 Aug. 16, 2004 665 Table_0665_GCB_459_A.txt 12,290,334 Aug. 16, 2004 666 Table_0666_GCB_459_B.txt 12,042,592 Aug. 12, 2004 667 Table_0667_GCB_470_A.txt 12,105,369 Aug. 13, 2004 668 Table_0668_GCB_470_B.txt 11,860,994 Aug. 16, 2004 669 Table_0669_GCB_479_A.txt 11,959,122 Aug. 16, 2004 670 Table_0870_GCB_479_B.txt 11,939,440 Aug. 13, 2004 671 Table_0671_GCB_492_A.txt 12,215,479 Aug. 16, 2004 672 Table_0672_GCB_492_B.txt 11,973,889 Aug. 13, 2004 673 Table_0673_GCB_517_A.txt 12,123,982 Aug. 16, 2004 674 Table_0674_GCB_517_B.txt 11,944,186 Aug. 13, 2004 675 Table_0675_GCB_523_A.txt 12,154,045 Aug. 16, 2004 676 Table_0676_GCB_523_B.txt 12,048,726 Aug. 13, 2004 677 Table_0677_GCB_524_A.txt 12,128,784 Aug. 16, 2004 678 Table_0678_GCB_524_B.txt 12,079,921 Aug. 16, 2004 679 Table_0679_GCB_529_A.txt 12,295,640 Aug. 12, 2004 680 Table_0680_GCB_529_B.txt 12,053,344 Aug. 13, 2004 681 Table_0681_GCB_533_A.txt 12,048,079 Aug. 12, 2004 682 Table_0682_GCB_533_B.txt 11,849,351 Aug. 16, 2004 Disc 13 of 22 683 Table_0683_GCB_537_A.txt 12,151,426 Aug. 16, 2004 684 Table_0684_GCB_537_B.txt 12,017,692 Aug. 16, 2004 685 Table_0685_GCB_543_A.txt 12,163,497 Aug. 13, 2004 686 Table_0686_GCB_543_B.txt 11,981,555 Aug. 13, 2004 687 Table_0687_GCB_545_A.txt 12,102,161 Aug. 12, 2004 688 Table_0688_GCB_545_B.txt 11,881,457 Aug. 13, 2004 689 Table_0689_GCB_549_A.txt 12,161,840 Aug. 13, 2004 690 Table_0690_GCB_549_B.txt 12,020,632 Aug. 13, 2004 691 Table_0691_GCB_550_A.txt 12,171,880 Aug. 12, 2004 692 Table_0692_GCB_550_B.txt 11,944,012 Aug. 13, 2004 693 Table_0693_GCB_553_A.txt 12,111,245 Aug. 16, 2004 694 Table_0694_GCB_553_B.txt 11,864,058 Aug. 13, 2004 695 Table_0695_GCB_565_A.txt 12,063,661 Aug. 13, 2004 696 Table_0696_GCB_565_B.txt 11,965,851 Aug. 13, 2004 697 Table_0697_GCB_572_A.txt 12,108,923 Aug. 16, 2004 698 Table_0698_GCB_572_B.txt 11,888,114 Aug. 16, 2004 699 Table_0699_GCB_617_A.txt 12,130,626 Aug. 13, 2004 700 Table_0700_GCB_617_B.txt 11,901,308 Aug. 13, 2004 701 Table_0701_GCB_618_A.txt 12,190,874 Aug. 12, 2004 702 Table_0702_GCB_618_B.txt 11,980,754 Aug. 16, 2004 703 Table_0703_GCB_619_A.txt 12,140,602 Aug. 13, 2004 704 Table_0704_GCB_619_B.txt 11,905,019 Aug. 16, 2004 705 Table_0705_GCB_623_A.txt 12,265,682 Aug. 13, 2004 706 Table_0706_GCB_623_B.txt 12,090,817 Aug. 13, 2004 707 Table_0707_GCB_627_A.txt 12,173,309 Aug. 16, 2004 708 Table_0708_GCB_627_B.txt 11,958,281 Aug. 16, 2004 709 Table_0709_GCB_654_A.txt 12,127,035 Aug. 13, 2004 710 Table_0710_GCB_654_B.txt 11,929,393 Aug. 16, 2004 711 Table_0711_GCB_661_A.txt 12,192,238 Aug. 12, 2004 712 Table_0712_GCB_661_B.txt 12,030,163 Aug. 13, 2004 713 Table_0713_GCB_669_A.txt 12,273,029 Aug. 16, 2004 714 Table_0714_GCB_669_B.txt 12,103,510 Aug. 12, 2004 715 Table_0715_GCB_672_A.txt 12,146,779 Aug. 13, 2004 716 Table_0716_GCB_672_B.txt 11,942,479 Aug. 13, 2004 717 Table_0717_GCB_674_A.txt 12,096,808 Aug. 13, 2004 718 Table_0718_GCB_674_B.txt 11,907,095 Aug. 13, 2004 719 Table_0719_GCB_675_A.txt 12,026,251 Aug. 16, 2004 720 Table_0720_GCB_675_B.txt 11,875,031 Aug. 13, 2004 721 Table_0721_GCB_681_A.txt 12,218,121 Aug. 16, 2004 722 Table_0722_GCB_681_B.txt 12,001,615 Aug. 13, 2004 723 Table_0723_GCB_688_A.txt 12,228,824 Aug. 16, 2004 724 Table_0724_GCB_688_B.txt 11,984,738 Aug. 13, 2004 725 Table_0725_GCB_695_A.txt 12,239,383 Aug. 13, 2004 726 Table_0726_GCB_695_B.txt 12,064,067 Aug. 13, 2004 727 Table_0727_GCB_698_A.txt 12,041,776 Aug. 13, 2004 728 Table_0728_GCB_698_B.txt 11,913,341 Aug. 13, 2004 729 Table_0729_GCB_701_A.txt 12,052,305 Aug. 13, 2004 730 Table_0730_GCB_701_B.txt 11,822,784 Aug. 12, 2004 731 Table_0731_GCB_710_A.txt 12,150,765 Aug. 13, 2004 732 Table_0732_GCB_710_B.txt 11,950,813 Aug. 12, 2004 733 Table_0733_GCB_711_A.txt 12,132,273 Aug. 12, 2004 734 Table_0734_GCB_711_B.txt 11,858,885 Aug. 13, 2004 735 Table_0735_GCB_722_A.txt 12,154,845 Aug. 13, 2004 736 Table_0736_GCB_722_B.txt 11,985,108 Aug. 13, 2004 737 Table_0737_GCB_724_A.txt 12,146,329 Aug. 12, 2004 738 Table_0738_GCB_724_B.txt 11,887,755 Aug. 13, 2004 Disc 14 of 22 739 Table_0739_GCB_731_A.txt 12,126,039 Aug. 13, 2004 740 Table_0740_GCB_731_B.txt 11,973,046 Aug. 13, 2004 741 Table_0741_GCB_742_A.txt 12,278,477 Aug. 12, 2004 742 Table_0742_GCB_742_B.txt 12,060,190 Aug. 12, 2004 743 Table_0743_GCB_744_A.txt 12,204,260 Aug. 13, 2004 744 Table_0744_GCB_744_B.txt 12,068,199 Aug. 13, 2004 745 Table_0745_GCB_745_A.txt 12,051,759 Aug. 12, 2004 746 Table_0746_GCB_745_B.txt 11,873,505 Aug. 13, 2004 747 Table_0747_GCB_747_A.txt 12,216,121 Aug. 12, 2004 748 Table_0748_GCB_747_B.txt 12,053,883 Aug. 12, 2004 749 Table_0749_GCB_749_A.txt 12,177,049 Aug. 13, 2004 750 Table_0750_GCB_749_B.txt 11,994,174 Aug. 12, 2004 751 Table_0751_GCB_758_A.txt 12,068,828 Aug. 12, 2004 752 Table_0752_GCB_758_B.txt 11,949,711 Aug. 12, 2004 753 Table_0753_GCB_772_A.txt 12,075,532 Aug. 12, 2004 754 Table_0754_GCB_772_B.txt 11,838,179 Aug. 12, 2004 755 Table_0755_GCB_777_A.txt 12,103,965 Aug. 13, 2004 756 Table_0756_GCB_777_B.txt 11,960,816 Aug. 13, 2004 757 Table_0757_GCB_792_A.txt 12,219,173 Aug. 12, 2004 758 Table_0758_GCB_792_B.txt 11,971,063 Aug. 12, 2004 759 Table_0759_GCB_795_A.txt 12,060,906 Aug. 13, 2004 760 Table_0760_GCB_795_B.txt 11,854,024 Aug. 12, 2004 761 Table_0761_GCB_797_A.txt 12,139,552 Aug. 12, 2004 762 Table_0762_GCB_797_B.txt 11,876,726 Aug. 13, 2004 763 Table_0763_GCB_803_A.txt 12,082,131 Aug. 13, 2004 764 Table_0764_GCB_803_B.txt 11,884,112 Aug. 12, 2004 765 Table_0765_GCB_810_A.txt 12,105,907 Aug. 12, 2004 766 Table_0766_GCB_810_B.txt 11,939,716 Aug. 12, 2004 767 Table_0767_GCB_817_A.txt 12,130,570 Aug. 12, 2004 768 Table_0768_GCB_817_B.txt 11,987,031 Aug. 12, 2004 769 Table_0769_GCB_818_A.txt 12,106,200 Aug. 12, 2004 770 Table_0770_GCB_818_B.txt 11,871,105 Aug. 13, 2004 771 Table_0771_GCB_819_A.txt 12,086,504 Aug. 13, 2004 772 Table_0772_GCB_819_B.txt 11,897,319 Aug. 12, 2004 773 Table_0773_GCB_821_A.txt 12,064,950 Aug. 13, 2004 774 Table_0774_GCB_821_B.txt 11,958,861 Aug. 12, 2004 775 Table_0775_GCB_832_A.txt 12,068,809 Aug. 12, 2004 776 Table_0776_GCB_832_B.txt 11,964,104 Aug. 13, 2004 777 Table_0777_GCB_836_A.txt 12,171,469 Aug. 13, 2004 778 Table_0778_GCB_836_B.txt 11,996,845 Aug. 13, 2004 779 Table_0779_GCB_840_A.txt 12,038,865 Aug. 12, 2004 780 Table_0780_GCB_840_B.txt 11,799,648 Aug. 12, 2004 781 Table_0781_GCB_847_A.txt 12,084,291 Aug. 13, 2004 782 Table_0782_GCB_847_B.txt 11,950,648 Aug. 13, 2004 783 Table_0783_GCB_1005_A.txt 12,100,992 Aug. 13, 2004 784 Table_0784_GCB_1005_B.txt 11,961,124 Aug. 12, 2004 785 Table_0785_GCB_1008_A.txt 12,084,812 Aug. 13, 2004 786 Table_0786_GCB_1008_B.txt 11,995,015 Aug. 13, 2004 787 Table_0787_GCB_1009_A.txt 11,943,142 Aug. 13, 2004 788 Table_0788_GCB_1009_B.txt 11,924,190 Aug. 13, 2004 789 Table_0789_GCB_1021_A.txt 12,113,951 Aug. 13, 2004 790 Table_0790_GCB_1021_B.txt 11,934,678 Aug. 13, 2004 791 Table_0791_GCB_1025_A.txt 12,101,376 Aug. 12, 2004 792 Table_0792_GCB_1025_B.txt 12,034,295 Aug. 12, 2004 793 Table_0793_GCB_1026_A.txt 11,998,844 Aug. 12, 2004 794 Table_0794_GCB_1026_B.txt 11,924,830 Aug. 13, 2004 795 Table_0795_GCB_1037_A.txt 12,102,751 Aug. 13, 2004 796 Table_0796_GCB_1037_B.txt 12,033,344 Aug. 12, 2004 797 Table_0797_GCB_1039_A.txt 12,138,545 Aug. 13, 2004 798 Table_0798_GCB_1039_B.txt 11,995,022 Aug. 13, 2004 799 Table_0799_GCB_1049_A.txt 11,910,885 Aug. 13, 2004 Disc 15 of 22 800 Table_0800_GCB_1049_B.txt 11,900,529 Aug. 12, 2004 801 Table_0801_GCB_1051_A.txt 12,096,658 Aug. 12, 2004 802 Table_0802_GCB_1051_B.txt 11,899,594 Aug. 12, 2004 803 Table_0803_GCB_1058_A.txt 12,014,086 Aug. 12, 2004 804 Table_0804_GCB_1058_B.txt 11,833,626 Aug. 13, 2004 805 Table_0805_GCB_1060_A.txt 12,148,321 Aug. 13, 2004 806 Table_0806_GCB_1060_B.txt 11,922,074 Aug. 12, 2004 807 Table_0807_GCB_1990_A.txt 12,081,238 Aug. 12, 2004 808 Table_0808_GCB_1990_B.txt 11,844,167 Aug. 13, 2004 809 Table_0809_GCB_1991_A.txt 12,099,680 Aug. 12, 2004 810 Table_0810_GCB_1991_B.txt 11,928,351 Aug. 12, 2004 811 Table_0811_GCB_2017_A.txt 12,118,675 Aug. 13, 2004 812 Table_0812_GCB_2017_B.txt 11,975,326 Aug. 13, 2004 813 Table_0813_GCB_2018_A.txt 11,925,112 Aug. 12, 2004 814 Table_0814_GCB_2018_B.txt 11,835,888 Aug. 13, 2004 815 Table_0815_GCB_2095_A.txt 11,853,266 Aug. 12, 2004 816 Table_0816_GCB_2095_B.txt 11,623,773 Aug. 12, 2004 817 Table_0817_GCB_880_A.txt 12,152,952 Aug. 13, 2004 818 Table_0818_GCB_860_B.txt 12,010,773 Aug. 13, 2004 819 Table_0819_GCB_871_A.txt 12,244,981 Aug. 13, 2004 820 Table_0820_GCB_871_B.txt 12,006,799 Aug. 12, 2004 821 Table_0821_GCB_874_A.txt 12,105,710 Aug. 12, 2004 822 Table_0822_GCB_874_B.txt 11,844,703 Aug. 13, 2004 823 Table_0823_GCB_995_A.txt 12,155,927 Aug. 12, 2004 824 Table_0824_GCB_995_B.txt 11,924,018 Aug. 13, 2004 825 Table_0825_LPC_2224_A.txt 12,213,302 Aug. 12, 2004 826 Table_0826_LPC_2224_B.txt 12,044,437 Aug. 13, 2004 827 Table_0827_LPC_2232_A.txt 12,166,888 Aug. 12, 2004 828 Table_0828_LPC_2232_B.txt 12,125,903 Aug. 12, 2004 829 Table_0829_LPC_2261_A.txt 12,228,399 Aug. 13, 2004 830 Table_0830_LPC_2261_B.txt 12,102,498 Aug. 12, 2604 831 Table_0831_LPC_2262_A.txt 12,312,435 Aug. 13, 2004 832 Table_0832_LPC_2262_B.txt 12,152,420 Aug. 12, 2004 833 Table_0833_LPC_2263_A.txt 12,224,752 Aug. 12, 2004 834 Table_0834_LPC_2263_B.txt 12,026,718 Aug. 12, 2004 835 Table_0835_LPC_2265_A.txt 12,285,962 Aug. 12, 2004 836 Table_0836_LPC_2265_B.txt 12,154,010 Aug. 12, 2004 837 Table_0837_Lymbl_2185_A.txt 12,108,958 Aug. 12, 2004 838 Table_0838_Lymbl_2185_B.txt 11,820,956 Aug. 12, 2004 839 Table_0839_Lymbl_2186_A.txt 12,308,145 Aug. 12, 2004 840 Table_0840_Lymbl_2186_B.txt 11,977,289 Aug. 12, 2004 841 Table_0841_Lymbl_2249_A.txt 12,199,046 Aug. 12, 2004 842 Table_0842_Lymbl_2249_B.txt 12,100,855 Aug. 12, 2004 843 Table_0843_MALT_gastric_2021_A.txt 12,069,340 Aug. 12, 2004 844 Table_0844_MALT_gastric_2021_B.txt 11,713,090 Aug. 12, 2004 845 Table_0845_MALT_gastric_2041_A.txt 12,022,811 Aug. 12, 2004 846 Table_0846_MALT_gastric_2041_B.txt 11,764,218 Aug. 12, 2004 847 Table_0847_MALT_gastric_2065_A.txt 12,078,944 Aug. 12, 2004 848 Table_0848_MALT_gastric_2065_B.txt 11,791,876 Aug. 12, 2004 849 Table_0849_MALT_gastric_2067_A.txt 12,202,669 Aug. 12, 2004 850 Table_0850_MALT_gastric_2067_B.txt 11,926,251 Aug. 12, 2004 851 Table_0851_MALT_gastric_2070_A.txt 11,759,597 Aug. 12, 2004 852 Table_0852_MALT_gastric_2070_B.txt 11,923,213 Aug. 12, 2004 853 Table_0853_MALT_gastric_2110_A.txt 11,989,061 Aug. 12, 2004 854 Table_0854_MALT_gastric_2110_B.txt 11,861,974 Aug. 12, 2004 855 Table_0855_MALT_gastric_2111_A.txt 11,875,847 Aug. 12, 2004 856 Table_0856_MALT_gastric_2111_B.txt 11,771,686 Aug. 12, 2004 857 Table_0857_MALT_gastric_2112_A.txt 11,987,235 Aug. 12, 2004 858 Table_0858_MALT_gastric_2112_B.txt 11,789,775 Aug. 12, 2004 859 Table_0859_MALT_gastric_2270_A.txt 12,163,208 Aug. 12, 2004 860 Table_0860_MALT_gastric_2270_B.txt 11,933,606 Aug. 12, 2004 Disc 16 of 22 861 Table_0861_MALT_lung_2202_A.txt 12,272,098 Aug. 12, 2004 862 Table_0862_MALT_lung_2202_B.txt 12,060,494 Aug. 12, 2004 863 Table_0863_MALT_salivary_2243_A.txt 12,192,735 Aug. 12, 2004 864 Table_0864_MALT_salivary_2243_B.txt 11,939,205 Aug. 12, 2004 865 Table_0865_MALT_tonsil_2211_A.txt 12,292,687 Aug. 12, 2004 866 Table_0866_MALT_tonsil_2211_B.txt 12,028,847 Aug. 12, 2004 867 Table_0867_MALT_unk_2178_A.txt 12,272,896 Aug. 12, 2004 868 Table_0868_MALT_unk_2178_B.txt 11,942,813 Aug. 12, 2004 869 Table_0869_MALT_unk_2182_A.txt 12,152,087 Aug. 12, 2004 870 Table_0870_MALT_unk_2182_B.txt 11,862,788 Aug. 12, 2004 871 Table_0871_MALT_unk_2183_A.txt 12,240,465 Aug. 12, 2004 872 Table_0872_MALT_unk_2183_B.txt 11,910,769 Aug. 12, 2004 873 Taable_0873_MALT_unk_2184_A.txt 12,131,543 Aug. 12, 2004 874 Table_0874_MALT_unk_2184_A.txt 11,790,493 Aug. 12, 2004 875 Table_0875_MCL_1012_A.txt 12,095,014 Aug. 12, 2004 876 Table_0876_MCL_1012_B.txt 12,009,170 Aug. 12, 2004 877 Table_0877_MCL_885_A.txt 12,252,079 Aug. 13, 2004 878 Table_0878_MCL_885_B.txt 12,003,985 Aug. 13, 2004 879 Table_0879_MCL_918_A.txt 12,067,434 Aug. 12, 2004 880 Table_0880_MCL_918_B.txt 11,913,221 Aug. 13, 2004 881 Table_0881_MCL_924_A.txt 12,132,102 Aug. 13, 2004 882 Table_0882_MCL_924_B.txt 11,963,278 Aug. 12, 2004 883 Table_0883_MCL_925_A.txt 12,192,477 Aug. 13, 2004 884 Table_0884_MCL_925_B.txt 11,969,040 Aug. 13, 2004 885 Table_0885_MCL_926_A.txt 12,235,529 Aug. 12, 2004 886 Table_0886_MCL_926_B.txt 12,047,299 Aug. 13, 2004 887 Table_0887_MCL_936_A.txt 12,241,467 Aug. 13, 2004 888 Table_0888_MCL_936_B.txt 12,045,327 Aug. 13, 2004 889 Table_0889_MCL_939_A.txt 12,202,107 Aug. 13, 2004 890 Table_0890_MCL_939_B.txt 11,958,874 Aug. 12, 2004 891 Table_0891_MCL_953_A.txt 11,892,010 Aug. 12, 2004 892 Table_0892_MCL_953_B.txt 11,760,154 Aug. 13, 2004 893 Table_0893_MCL_956_A.txt 12,251,157 Aug. 13, 2004 894 Tabte_0894_MCL_956_B.txt 12,111,836 Aug. 12, 2004 895 Table_0895_MCL_964_A.txt 12,275,186 Aug. 13, 2004 898 Table_0896_MCL_964_B.txt 12,133,152 Aug. 12, 2004 897 Table_0897_MCL_966_A.txt 12,259,135 Aug. 12, 2004 898 Table_0898_MCL_966_B.txt 12,024,326 Aug. 13, 2004 899 Table_0899_MCL_968_A.txt 12,155,882 Aug. 13, 2004 900 Table_0900_MCL_968_B.txt 11,994,843 Aug. 13, 2004 901 Table_0901_MCL_970_A.txt 12,088,529 Aug. 13, 2004 902 Table_0902_MCL_970_B.txt 11,954,394 Aug. 13, 2004 903 Table_0903_MCL_1091_A.txt 12,094,827 Aug. 13, 2004 904 Table_0904_MCL_1091_B.txt 11,874,993 Aug. 13, 2004 905 Table_0905_MCL_1114_A.txt 12,245,076 Aug. 13, 2004 906 Table_0906_MCL_1114_B.txt 12,198,851 Aug. 13, 2004 907 Table_0907_MCL_1128_A.txt 12,001,197 Aug. 13, 2004 908 Table_0908_MCL_1128_B.txt 11,988,339 Aug. 12, 2004 909 Table_0909_MCL_1150_A.txt 12,152,268 Aug. 12, 2004 910 Table_0910_MCL_1150_B.txt 11,985,382 Aug. 13, 2004 911 Table_0911_MCL_1162_A.txt 12,052,700 Aug. 13, 2004 912 Table_0912_MCL_1162_B.txt 11,813,424 Aug. 13, 2004 913 Table_0913_MCL_1166_A.txt 12,154,210 Aug. 13, 2004 914 Table_0914_MCL_1166_B.txt 11,978,362 Aug. 13, 2004 915 Table_1915_MCL_1194_A.txt 12,219,860 Aug. 12, 2004 916 Table_0916_MCL_1194_B.txt 11,956,615 Aug. 12, 2004 917 Table_0917_Mult_Myeloma_H1112_100001_A.txt 11,811,143 Aug. 12, 2004 918 Table_0918_Mult_Myeloma_H1112_100001_B.txt 1,607,110 Aug. 12, 2004 919 Table_0919_Mult_Myeloma_HDLM2_100002_A.txt 12,201,407 Aug. 12, 2004 920 Table_0920_Mult_Myeloma_HDLM2_100002_B.txt 12,064,162 Aug. 12, 2004 921 Table_0921_Mult_Myeloma_JIM3_100003_A.txt 12,018,803 Aug. 12, 2004 Disc 17 of 22 922 Table_0922_Mult_Myeloma_JJM3_100003_B.txt 11,829,274 Aug. 12, 2004 923 Table_0923_Mult_Myeloma_JJN3_100004_A.txt 11,938,479 Aug. 12, 2004 924 Table_0924_Mult_Myeloma_JJN3_100004_B.txt 11,783,677 Aug. 12, 2004 925 Table_0925_Mult_Myeloma_KMS11_100005_A.txt 12,049,424 Aug. 12, 2004 926 Table_0926_Mult_Myeloma_KMS11_100005_B.txt 11,830,659 Aug. 12, 2004 927 Table_0927_Mult_Myeloma_KMS12_100008_A.txt 11,987,299 Aug. 12, 2004 928 Table_0928_Mult_Myeloma_KMS12_100006_B.txt 11,843,592 Aug. 12, 2004 929 Table_0929_MuIt_Myeloma_LB84_100007_A.txt 12,013,517 Aug. 12, 2004 930 Table_0930_Mult_Myeloma_LB84_100007_B.txt 11,779,980 Aug. 12, 2004 931 Table_0931_Mult_Myeloma_LP1_100008_A.txt 11,881,584 Aug. 12, 2004 932 Table_0932_Mult_Myeloma_LP1_100008_B.txt 11,697,789 Aug. 12, 2004 933 Table_0933_Mult_Myeloma_U266_100009_A.txt 11,911,589 Aug. 12, 2004 934 Table_0934_Mult_Myeloma_U266_100009_B.txt 11,746,068 Aug. 12, 2004 935 Table_0935_NMZ_2231_A.txt 12,102,948 Aug. 12, 2004 936 Table_0936_NMZ_2231_B.txt 11,924,657 Aug. 13, 2004 937 Table_0937_NMZ_2242_A.txt 12,155,728 Aug. 13, 2004 938 Table_0938_NMZ_2242_B.txt 11,868,614 Aug. 12, 2004 939 Table_0939_NMZ_2254_A.txt 12,192,220 Aug. 12, 2004 940 Table_0940_NMZ_2254_B.txt 11,993,870 Aug. 13, 2004 941 Table_0941_PMBL_484_A.txt 12,082,526 Aug. 12, 2004 942 Table_0942_PMBL_484_B.txt 11,815,878 Aug. 12, 2004 943 Table_0943_PMBL_546_A.txt 12,246,074 Aug. 12, 2004 944 Table_0944_PMBL_546_B.txt 12,018,016 Aug. 13, 2004 945 Table_0945_PMBL_570_A.txt 12,022,732 Aug. 13, 2004 946 Table_0946_PMBL_570_B.txt 11,987,920 Aug. 12, 2004 947 Table_0947_PMBL_621_A.txt 12,274,976 Aug. 13, 2004 948 Table_0948_PMBL_621_B.txt 11,990,316 Aug. 12, 2004 949 Table_0949_PMBL_638_A.txt 12,127,006 Aug. 12, 2004 950 Table_0950_PMBL_638_B.txt 11,914,175 Aug. 13, 2004 951 Table_0951_PMBL_691_A.txt 12,212,873 Aug. 13, 2004 952 Table_0952_PMBL_691_B.txt 11,914,318 Aug. 12, 2004 953 Table_0953_PMBL_791_A.txt 12,097,450 Aug. 13, 2004 954 Table_0954_PMBL_791_B.txt 11,992,010 Aug. 13, 2004 955 Table_0955_PMBL_824_A.txt 12,088,222 Aug. 12, 2004 956 Table_0956_PMBL_824_B.txt 11,955,158 Aug. 12, 2004 957 Table_0957_PMBL_906_A.txt 12,132,964 Aug. 13, 2004 958 Table_0958_PMBL_906_B.txt 11,995,056 Aug. 12, 2004 959 Table_0959_PMBL_994_A.txt 12,174,956 Aug. 13, 2004 960 Table_0960_PMBL_994_B.txt 11,819,415 Aug. 12, 2004 961 Table_0961_PMBL_998_A.txt 12,084,009 Aug. 13, 2004 962 Table_0962_PMBL_998_B.txt 11,893,733 Aug. 12, 2004 963 Table_0963_PMBL_1006_A.txt 12,028,424 Aug. 10, 2004 964 Table_0964_PMBL_1006_B.txt 11,935,369 Aug. 10, 2004 965 Table_0965_PMBL_1024_A.txt 11,957,270 Aug. 10, 2004 966 Table_0966_PMBL_1024_B.txt 11,764,809 Aug. 10, 2004 967 Table_0967_PMBL_1048_A.txt 11,897,468 Aug. 10, 2004 968 Table_0968_PMBL_1048_B.txt 11,883,251 Aug. 10, 2004 969 Table_0969_PMBL_1053_A.txt 11,875,959 Aug. 10, 2004 970 Table_0970_PMBL_1053_B.txt 11,710,774 Aug. 10, 2004 971 Table_0971_PMBL_1920_A.txt 12,226,172 Aug. 10, 2004 972 Table_0972_PMBL_1920_B.txt 12,193,948 Aug. 10, 2004 973 Table_0973_PMBL_1921_A.txt 12,143,375 Aug. 10, 2004 974 Table_0974_PMBL_1921_B.txt 11,908,102 Aug. 10, 2004 975 Table_0975_PMBL_1923_A.txt 12,198,159 Aug. 10, 2004 976 Table_0976_PMBL_1923_B.txt 11,946,971 Aug. 10, 2004 977 Table_0977_PMBL_1924_A.txt 12,122,855 Aug. 10, 2004 978 Table_0978_PMBL_1924_B.txt 11,890,275 Aug. 10, 2004 979 Table_0979_PMBL_1935_A.txt 12,068,019 Aug. 10, 2004 980 Table_0980_PMBL_1935_B.txt 11,985,509 Aug. 10, 2004 981 Table_0981_PMBL_1941_A.txt 12,119,223 Aug. 10, 2004 982 Table_0982_PMBL_1941_B.txt 11,930,039 Aug. 10, 2004 Disc 18 of 22 983 Table_0983_PMBL_1942_A.txt 12,067,442 Aug. 10, 2004 984 Table_0984_PMBL_1942_B.txt 11,920,274 Aug. 10, 2004 985 Table_0985_PMBL_1943_A.txt 12,071,233 Aug. 10, 2004 986 Table_0986_PMBL_1943_B.txt 11,873,515 Aug. 10, 2004 987 Table_0987_PMBL_1945_A.txt 12,127,286 Aug. 10, 2004 988 Table_0988_PMBL_1945_B.txt 11,893,206 Aug. 10, 2004 989 Table_0989_PMBL_1948_A.txt 12,166,081 Aug. 10, 2004 990 Table_0990_PMBL_1948_B.txt 11,844,013 Aug. 10, 2004 991 Table_0991_PMBL_1949_A.txt 12,127,499 Aug. 10, 2004 992 Table_0992_PMBL_1949_B.txt 12,009,898 Aug. 10, 2004 993 Table_0993_PMBL_1989_A.txt 11,979,711 Aug. 10, 2004 994 Table_0994_PMBL_1989_B.txt 11,831,156 Aug. 10, 2004 995 Table_0995_PMBL_1992_A.txt 11,982,288 Aug. 10, 2004 996 Table_0996_PMBL_1992_B.txt 11,805,053 Aug. 10, 2004 997 Table_0997_PMBL_1993_A.txt 11,965,853 Aug. 10, 2004 998 Table_0998_PMBL_1993_B.txt 11,781,282 Aug. 10, 2004 999 Table_0999_PMBL_2002_A.txt 12,145,467 Aug. 10, 2004 1000 Table_1000_PMBL_2002_B.txt 11,908,468 Aug. 10, 2004 1001 Table_1001_PMBL_2019_A.txt 12,060,714 Aug. 10, 2004 1002 Table_1002_PMBL_2019_B.txt 11,835,919 Aug. 10, 2004 1003 Table_1003_PMBL_2020_A.txt 11,923,777 Aug. 10, 2004 1004 Table_1004_PMBL_2020_B.txt 11,763,296 Aug. 10, 2004 1005 Table_1005_PMBL_2092_A.txt 11,790,019 Aug. 10, 2004 1006 Table_1006_PMBL_2092_B.txt 11,681,328 Aug. 10, 2004 1007 Table_1007_PTLD_1817_A.txt 11,963,639 Aug. 10, 2004 1008 Table_1008_PTLD_1817_B.txt 11,841,407 Aug. 10, 2004 1009 Table_1009_PTLD_1824_A.txt 11,948,464 Aug. 10, 2004 1010 Table_1010_PTLD_1824_B.txt 11,908,800 Aug. 10, 2004 1011 Table_1011_PTLD_1981_A.txt 12,117,957 Aug. 10, 2004 1012 Table_1012_PTLD_1981_B.txt 11,889,503 Aug. 10, 2004 1013 Table_1013_PTLD_1986_A.txt 12,190,747 Aug. 10, 2004 1014 Table_1014_PTLD_1986_B.txt 11,999,062 Aug. 10, 2004 1015 Table_1015_SLL_1151_A.txt 12,137,203 Aug. 10, 2004 1016 Table_1016_SLL_1151_B.txt 12,068,815 Aug. 10, 2004 1017 Table_1017_SLL_1153_A.txt 12,167,324 Aug. 10, 2004 1018 Table_1018_SLL_1153_B.txt 11,973,110 Aug. 10, 2004 1019 Table_1019_SLL_1155_A.txt 12,014,085 Aug. 10, 2004 1020 Table_1020_SLL_1155_B.txt 11,972,994 Aug. 10, 2004 1021 Table_1021_SLL_1156_A.txt 12,049,347 Aug. 10, 2004 1022 Table_1022_SLL_1156_B.txt 12,067,063 Aug. 10, 2004 1023 Table_1023_SLL_1158_A.txt 12,133,241 Aug. 13, 2004 1024 Table_1024_SLL_1158_B.txt 11,956,872 Aug. 13, 2004 1025 Table_1025_SLL_1213_A.txt 12,143,857 Aug. 13, 2004 1026 Table_1026_SLL_1213_B.txt 12,011,763 Aug. 13, 2004 1027 Table_1027_SLL_1214_A.txt 12,045,032 Aug. 13, 2004 1028 Table_1028_SLL_1214_B.txt 12,031,831 Aug. 13, 2004 1029 Table_1029_SLL_1216_A.txt 11,833,813 Aug. 13, 2004 1030 Table_1030_SLL_1216_B.txt 11,772,139 Aug. 13, 2004 1031 Table_1031_SLL_1217_A.txt 12,094,981 Aug. 12, 2004 1032 Table_1032_SLL_1217_B.txt 11,937,188 Aug. 12, 2004 1033 Table_1033_SLL_1218_A.txt 11,940,236 Aug. 13, 2004 1034 Table_1034_SLL_1218_B.txt 11,959,292 Aug. 13, 2004 1035 Table_1035_SLL_1220_A.txt 12,120,053 Aug. 13, 2004 1036 Table_1036_SLL_1220_B.txt 12,048,618 Aug. 13, 2004 1037 Table_1037_SLL_1229_A.txt 12,163,710 Aug. 13, 2004 Disc 19 of 22 1038 Table_1038_SLL_1229_B.txt 12,055,954 Aug. 13, 2004 1039 Table_1039_SLL_1231_A.txt 12,991,514 Aug. 12, 2004 1040 Table_1040_SLL_1231_B.txt 12,005,235 Aug. 13, 2004 1041 Table_1041_SLL_1235_A.txt 12,080,267 Aug. 13, 2904 1042 Table_1042_SLL_1235_B.txt 11,994,892 Aug. 12, 2004 1043 Table_1043_SLL_1236_A.txt 12,084,789 Aug. 13, 2004 1044 Table_1044_SLL_1236_B.txt 12,026,667 Aug. 13, 2004 1045 Table_1045_SLL_1238_A.txt 12,082,819 Aug. 12, 2004 1046 Table_1046_SLL_1238_B.txt 11,974,701 Aug. 13, 2004 1047 Table_1047_SLL_1240_A.txt 12,107,426 Aug. 13, 2004 1048 Table_1048_SLL_1240_B.txt 12,007,704 Aug. 12, 2004 1049 Table_1049_SPLENIC_2061_A.txt 12,008,092 Aug. 12, 2004 1050 Table_1050_SPLENIC_2061_B.txt 11,899,642 Aug. 12, 2004 1051 Table_1051_SPLENIC_2074_A.txt 11,955,622 Aug. 12, 2004 1052 Table_1052_SPLENIC_2074_B.txt 11,890,462 Aug. 12, 2004 1053 Table_1053_SPLENIC_2075_A.txt 11,810,049 Aug. 12, 2004 1954 Table_1054_SPLENIC_2075_B.txt 11,772,595 Aug. 12, 2004 1055 Table_1055_SPLENIC_2077_A.txt 12,051,083 Aug. 12, 2004 1056 Table_1056_SPLENIC_2077_B.txt 11,816,491 Aug. 12, 2004 1057 Table_1057_SPLENIC_2079_A.txt 12,028,337 Aug. 13, 2004 1058 Table_1058_SPLENIC_2079_B.txt 11,666,448 Aug. 12, 2004 1059 Table_1059_SPLENIC_2104_A.txt 11,964,762 Aug. 12, 2004 1060 Table_1060_SPLENIC_2104_B.txt 11,883,377 Aug. 12, 2004 1061 Table_1061_SPLENIC_2105_A.txt 12,027,922 Aug. 13, 2004 1062 Table_1062_SPLENIC_2105_B.txt 11,816,300 Aug. 12, 2004 1063 Table_1063_SPLENIC_2106_A.txt 11,927,458 Aug. 12, 2004 1064 Table_1064_SPLENIC_2106_B.txt 11,707,237 Aug. 13, 2004 1065 Table_1065_SPLENIC_2256_A.txt 12,134,849 Aug. 12, 2004 1066 Table_1066_SPLENIC_2256_B.txt 12,000,432 Aug. 12 2004 1067 Table_1067_SPLENIC_2257_A.txt 12,095,918 Aug. 12, 2004 1068 Table_1088_SPLENIC_2257_B.txt 11,860,498 Aug. 12, 2004 1069 Table_1069_SPLENIC_2258_A.txt 12,173,033 Aug. 12, 2004 1070 Table_1070_SPLENIC_2258_B.txt 12,097,740 Aug. 13, 2004 1071 Table_1071_SPLENIC_2259_A.txt 12,070,761 Aug. 12, 2004 1072 Table_1072_SPLENIC_2259_B.txt 11,905,682 Aug. 12, 2004 1073 Table_1073_SPLENIC_2260_A.txt 12,218,038 Aug. 12, 2004 1074 Table_1074_SPLENIC_2260_B.txt 11,987,269 Aug. 13, 2004 1075 Table_1075_UC_DLBCL_306_A.txt 12,197,523 Aug. 12, 2004 1076 Table_1076_UC_DLBCL_306_B.txt 12,022,523 Aug. 13, 2004 1077 Table_1077_UC_DLBCL_310_A.txt 12,200,397 Aug. 12, 2004 1078 Table_1078_UC_DLBCL_310_B.txt 12,031,998 Aug. 12, 2004 1079 Table_1079_UC_DLBCL_449_A.txt 12,190,478 Aug. 12, 2004 1080 Table_1080_UC_DLBCL_449_B.txt 11,975,216 Aug. 12, 2004 1081 Table_1081_UC_DLBCL_452_A.txt 12,131,303 Aug. 12, 2004 1082 Table_1082_UC_DLBCL_452_B.txt 11,894,8Z0 Aug. 13, 2004 1083 Table_1083_UC_DLBCL_1001_A.txt 12,196,485 Aug. 12, 2004 1084 Table_1084_UC_DLBCL_1001_B.txt 11,918,908 Aug. 13, 2004 1085 Table_1085_UC_DLBCL_1004_A.txt 12,085,125 Aug. 12, 2004 1086 Table_1086_UC_DLBCL_1004_B.txt 11,976,628 Aug. 13, 2004 1087 Table_1087_UC_DLBCL_1007_A.txt 12,008,629 Aug. 13, 2004 1088 Table_1088_UC_DLBCL_1007_B.txt 11,894,817 Aug. 12, 2004 1089 Table_1089_UC_DLBCL_1018_A.txt 11,978,212 Aug. 12, 2004 1090 Table_1090_UC_DLBCL_1018_B.txt 11,779,082 Aug. 12, 2004 1091 Table_1091_UC_DLBCL_1041_A.txt 12,075,817 Aug. 12, 2004 1092 Table_1092_UC_DLBCL_1041_B.txt 11,954,441 Aug. 13, 2004 1093 Table_1093_UC_DLBCL_1054_A.txt 12,072,956 Aug. 13, 2004 1094 Table_1094_UC_DLBCL_1054_B.txt 11,888,591 Aug. 12, 2004 1095 Table_1095_UC_DLBCL_1946_A.txt 12,146,234 Aug. 12, 2004 1096 Table_1096_UC_DLBCL_1946_B.txt 11,972,987 Aug. 13, 2004 1097 Table_1097_UC_DLBCL_458_A.txt 12,259,814 Aug. 12, 2004 1098 Table_1098_UC_DLBCL_458_B.txt 12,079,665 Aug. 12, 2004 Disc 20 of 22 1099 Table_1099_UC_DLBCL_460_A.txt 12,291,473 Aug. 12, 2004 1100 Table_1100_UC_DLBCL_460_B.txt 12,152,157 Aug. 12, 2004 1101 Table_1101_UC_DLBCL_491_A.txt 12,1Z7,657 Aug. 12, 2004 1102 Table_1102_UC_DLBCL_491_B.txt 11,939,054 Aug. 12, 2004 1103 Table_1103_UC_DLCBL_528_A.txt 12,222,657 Aug. 12, 2004 1104 Table_1104_UC_DLCBL_528_B.txt 12,030,161 Aug. 12, 2004 1105 Table_1105_UC_DLCBL_615_A.txt 12,093,006 Aug. 12, 2004 1106 Table_l106_UC_DLCBL_615_B.txt 11,905,401 Aug. 12, 2004 1107 Table_1107_UC_DLCBL_625_A.txt 12,182,746 Aug. 12, 2004 1108 Table_1108_UC_DLCBL_625_B.txt 11,978,584 Aug. 12, 2004 1109 Table_1109_UC_DLCBL_664_A.txt 12,147,340 Aug. 12, 2004 1110 Table_1110_UC_DLCBL_664_B.txt 11,921,611 Aug. 12, 2004 1111 Table_1111_UC_DLCBL_671_A.txt 12,235,865 Aug. 12, 2004 1112 Table_1112_UC_DLCBL_671_B.txt 12,025,944 Aug. 12, 2004 1113 Table_1113_UC_DLCBL_682_A.txt 12,139,932 Aug. 12, 2004 1114 Table_1114_UC_DLCBL_682_B.txt 11,999,845 Aug. 12, 2004 1115 Table_1115_UC_DLCBL_683_A.txt 12,159,705 Aug. 12, 2004 1116 Table_1116_UC_DLCBL_683_B.txt 11,904,634 Aug. 12, 2004 1117 Table_1117_UC_DLCBL_684_A.txt 12,172,978 Aug. 12, 2004 1118 Table_1118_UC_DLCBL_684_B.txt 11,926,785 Aug. 12, 2004 1119 Table_1119_UC_DLCBL_748_A.txt 12,035,936 Aug. 12, 2004 1120 Table_1120_UC_DLCBL_748_B.txt 11,907,789 Aug. 12, 2004 1121 Table_1707_UC_DLCBL_751_A.txt 12,044,311 Aug. 12, 2004 1122 Table_1708_UC_DLCBL_751_B.txt 11,684,690 Aug. 12, 2004 1123 Table_1709_UC_DLCBL_808_A.txt 12,173,769 Aug. 12, 2004 1124 Table_1710_UC_DLCBL_808_B.txt 11,894,695 Aug. 12, 2004 1125 Table_1711_UC_DLCBL_831_A.txt 12,093,914 Aug. 12, 2004 1126 Table_1712_UC_DLCBL_831_B.txt 11,864,674 Aug. 12, 2004 1127 Table_1713_UC_DLCBL_834_A.txt 12,104,086 Aug. 12, 2004 1128 Table_1714_UC_DLCBL_834_B.txt 11,923,742 Aug. 12, 2004 1129 Table_1715_UC_DLCBL_838_A.txt 12,098,316 Aug. 12, 2004 1130 Table_1716_UC_DLCBL_838_B.txt 11,966,988 Aug. 12, 2004 1131 Table_1717_UC_DLCBL_851_A.txt 12,228,013 Aug. 12, 2004 1132 Table_1718_UC_DLCBL_851_B.txt 12,052,993 Aug. 12, 2004 1133 Table_1719_UC_DLCBL_854_A.txt 12,265,143 Aug. 12, 2004 1134 Table_1720_UC_DLCBL_854_B.txt 12,132,832 Aug. 12, 2004 1135 Table_1721_UC_DLCBL_855_A.txt 12,008,306 Aug. 12, 2004 1136 Table_1722_UC_DLCBL_855_B.txt 11,859,284 Aug. 12, 2004 1137 Table_1723_UC_DLCBL_856_A.txt 12,049,127 Aug. 12, 2004 1138 Table_1138_UC_DLCBL_856_B.txt 11,827,365 Aug. 12, 2004 1139 Table_1139_ABC_1000_log_signal.txt 719,801 Aug. 5, 2004 1140 Table_1140_ABC_1002_log_signal.txt 719,835 Aug. 5, 2004 1141 Table_1141_ABC_1023_log_signal.txt 719,881 Aug. 5, 2004 1142 Table_1142_ABC_1027_log_signal.txt 719,747 Aug. 5, 2004 1143 Table_1143_ABC_1031_log_signal.txt 719,600 Aug. 5, 2004 1144 Table_1144_ABC_1034_log_signal.txt 719,676 Aug. 5, 2004 1145 Table_1145_ABC_1038_log_signal.txt 719,775 Aug. 5, 2004 1146 Table_1146_ABC_1043_log_signal.txt 719,870 Aug. 5, 2004 1147 Table_1147_ABC_1045_log_signal.txt 719,737 Aug. 5, 2004 1148 Table_1148_ABC_1055_log_signal.txt 719,776 Aug. 5, 2004 1149 Table_1149_ABC_1057_log_signal.txt 719,889 Aug. 5, 2004 1150 Table_1150_ABC_1059_log_signal.txt 719,667 Aug. 5, 2004 1151 Table_1151_ABC_1061_log_signal.txt 719,846 Aug. 5, 2004 1152 Table_1152_ABC_1946_log_signal.txt 719,287 Aug. 5, 2004 1153 Table_1153_ABC_1994_log_signal.txt 719,569 Aug. 5, 2004 1154 Table_1154_ABC_2001_log_signal.txt 719,782 Aug. 5, 2004 1155 Table_1155_ABC_304_log_signal.txt 719,749 Aug. 5, 2004 1156 Table_1156_ABC_305_log_signal.txt 719,882 Aug. 5, 2004 1157 Table_1157_ABC_309_log_signal.txt 719,614 Aug. 5, 2004 1158 Table_1158_ABC_413_log_signal.txt 719,850 Aug. 5, 2004 1159 Table_1159_ABC_428_log_signal.txt 719,605 Aug. 5, 2004 1160 Table_1160_ABC_432_log_signal.txt 719,814 Aug. 5, 2004 1161 Table_1161_ABC_446_log_signal.txt 719,655 Aug. 5, 2004 1162 Table_1162_ABC_462_log_signal.txt 719,759 Aug. 5, 2004 1163 Table_1163_ABC_477_log_signal.txt 719,684 Aug. 5, 2004 1164 Table_1164_ABC_481_log_signal.txt 719,824 Aug. 5, 2004 1165 Table_1165_ABC_482_log_signal.txt 719,812 Aug. 5, 2004 1166 Table_1166_ABC_538_log_signal.txt 719,582 Aug. 5, 2004 1167 Table_1167_ABC_539_log_signal.txt 719,812 Aug. 5, 2004 1168 Table_1168_ABC_544_log_signal.txt 719,917 Aug. 5, 2004 1169 Table_1169_ABC_547_log_signal.txt 719,719 Aug. 5, 2004 1170 Table_1170_ABC_577_log_signal.txt 719,622 Aug. 5, 2004 1171 Table_1171_ABC_616_log_signal.txt 719,564 Aug. 5, 2004 1172 Table_1172_ABC_626_log_signal.txt 719,672 Aug. 5, 2004 1173 Table_1173_ABC_633_log_signal.txt 719,756 Aug. 5, 2004 1174 Table_1174_ABC_642_log_signal.txt 719,791 Aug. 5, 2004 1175 Table_1175_ABC_644_log_signal.txt 719,998 Aug. 5, 2004 1176 Table_1176_ABC_645_log_signal.txt 719,824 Aug. 5, 2004 1177 Table_1177_ABC_646_log_signal.txt 719,780 Aug. 5, 2004 1178 Table_1178_ABC_651_log_signal.txt 719,979 Aug. 5, 2004 1179 Table_1179_ABC_652_log_signal.txt 719,932 Aug. 5, 2004 1180 Table_1180_ABC_660_log_signal.txt 719,428 Aug. 5, 2004 1181 Table_1181_ABC_663_log_signal.txt 719,951 Aug. 5, 2004 1182 Table_1182_ABC_668_log_signal.txt 719,742 Aug. 5, 2004 1183 Table_1183_ABC_676_log_signal.txt 719,842 Aug. 5, 2004 1184 Table_1184_ABC_678_log_signal.txt 719,668 Aug. 5, 2004 1185 Table_1185_ABC_687_log_signal.txt 719,947 Aug. 5, 2004 1186 Table_1186_ABC_689_log_signal.txt 719,928 Aug. 5, 2004 1187 Table_1187_ABC_692_log_signal.txt 719,778 Aug. 5, 2004 1188 Table_1188_ABC_694_log_signal.txt 719,758 Aug. 5, 2004 1189 Table_1189_ABC_700_log_signal.txt 719,746 Aug. 5, 2004 1190 Table_1190_ABC_702_log_signal.txt 719,782 Aug. 5, 2004 1191 Table_1191_ABC_704_log_signal.txt 719,787 Aug. 5, 2004 1192 Table_1192_ABC_709_log_signal.txt 719,756 Aug. 5, 2004 1193 Table_1193_ABC_712_log_signal.txt 719,071 Aug. 5, 2004 1194 Table_1194_ABC_714_log_signal.txt 719,901 Aug. 5, 2004 1195 Table_1195_ABC_717_log_signal.txt 719,580 Aug. 5, 2004 1196 Table_1196_ABC_725_log_signal.txt 719,602 Aug. 5, 2004 1197 Table_1197_ABC_726_log_signal.txt 719,595 Aug. 5, 2004 1198 Table_1198_ABC_730_log_signal.txt 719,857 Aug. 5, 2004 1199 Table_1199_ABC_753_log_signal.txt 719,754 Aug. 5, 2004 1200 Table_1200_ABC_756_log_signal.txt 719,818 Aug. 5, 2004 1201 Table_1201_ABC_771_log_signal.txt 719,890 Aug. 5, 2004 1202 Table_1202_ABC_779_log_signal.txt 719,647 Aug. 5, 2004 1203 Table_1203_ABC_789_log_signal.txt 719,947 Aug. 5, 2004 1204 Table_1204_ABC_800_log_signal.txt 719,536 Aug. 5, 2004 1205 Table_1205_ABC_807_log_signal.txt 719,798 Aug. 5, 2004 1206 Table_1206_ABC_809_log_signal.txt 719,863 Aug. 5, 2004 1207 Table_1207_ABC_816_log_signal.txt 719,339 Aug. 5, 2004 1208 Table_1208_ABC_820_log_signal.txt 719,562 Aug. 5, 2004 1209 Table_1209_ABC_823_log_signal.txt 719,555 Aug. 5, 2004 1210 Table_1210_ABC_835_log_signal.txt 719,584 Aug. 5, 2004 1211 Table_1211_ABC_839_log_signal.txt 719,866 Aug. 5, 2004 1212 Table_1212_ABC_841_log_signal.txt 719,900 Aug. 5, 2004 1213 Table_1213_ABC_858_log_signal.txt 719,601 Aug. 5, 2004 1214 Table_1214_ABC_872_log_signal.txt 719,762 Aug. 5, 2004 1215 Table_1215_ABC_875_log_signal.txt 719,888 Aug. 5, 2004 1216 Table_1216_ABC_912_log_signal.txt 719,587 Aug. 5, 2004 1217 Table_1217_ABC_996_log_signal.txt 719,844 Aug. 5, 2004 1218 Table_1218_BL_2032_log_signal.txt 719,439 Aug. 5, 2004 1219 Table_1219_BL_2033_log_signal.txt 719,466 Aug. 5, 2004 1220 Table_1220_BL_2035_log_signal.txt 719,468 Aug. 5, 2004 1221 Table_1221_BL_2036_log_signal.txt 719,517 Aug. 5, 2004 1222 Table_1222_BL_2037_log_signal.txt 720,059 Aug. 5, 2004 1223 Table_1223_BL_2038_log_signal.txt 719,518 Aug. 5, 2004 1224 Table_1224_BL_2082_log_signal.txt 719,809 Aug. 5, 2004 1225 Table_1225_BL_2083_log_signal.txt 719,328 Aug. 5, 2004 1226 Table_1226_BL_2086_log_signal.txt 719,338 Aug. 5, 2004 1227 Table_1227_BL_2088_log_signal.txt 719,656 Aug. 5, 2004 1228 Table_1228_BL_2090_log_signal.txt 719,526 Aug. 5, 2004 1229 Table_1229_BL_2091_log_signal.txt 719,721 Aug. 5, 2004 1230 Table_1230_BL_2097_log_signal.txt 719,754 Aug. 5, 2004 1231 Table_1231_BL_2099_log_signal.txt 719,574 Aug. 5, 2004 1232 Table_1232_BL_2100_log_signal.txt 719,808 Aug. 5, 2004 1233 Table_1233_BL_2101_log_signal.txt 719,733 Aug. 5, 2004 1234 Table_1234_BL_2103_log_signal.txt 719,692 Aug. 5, 2004 1235 Table_1235_BL_2125_log_signal.txt 719,778 Aug. 5, 2004 1236 Table_1236_BL_2126_log_signal.txt 719,298 Aug. 5, 2004 1237 Table_1237_BL_2127_log_signal.txt 719,313 Aug. 5, 2004 1238 Table_1238_BL_2128_log_signal.txt 719,339 Aug. 5, 2004 1239 Table_1239_BL_2129_log_signal.txt 719,494 Aug. 5, 2004 1240 Table_1240_BL_2267_log_signal.txt 719,913 Aug. 5, 2004 1241 Table_1241_BL_2268_log_signal.txt 719,897 Aug. 5, 2004 1242 Table_1242_BL_2269_log_signal.txt 719,982 Aug. 5, 2004 1243 Table_1243_BL_2271_log_signal.txt 719,810 Aug. 5, 2004 1244 Table_1244_CDnegMCL_1013_log_signal.txt 719,496 Aug. 5, 2004 1245 Table_1245_CDnegMCL_1116_log_signal.txt 719,808 Aug. 5, 2004 1246 Table_1246_CDnegMCL_1125_log_signal.txt 719,557 Aug. 5, 2004 1247 Table_1247_CDnegMCL_1265_log_signal.txt 719,740 Aug. 5, 2004 1248 Table_1248_CDnegMCL_2198_log_signal.txt 720,028 Aug. 5, 2004 1249 Table_1249_CDnegMCL_2272_log_signal.txt 719,923 Aug. 5, 2004 1250 Table_1250_CDnegMCL_930_log_signal.txt 719,949 Aug. 5, 2004 1251 Table_1251_CDnegMCL_950_log_signal.txt 719,891 Aug. 5, 2004 1252 Table_1252_CDnegMCL_985_log_signal.txt 719,797 Aug. 5, 2004 1253 Table_1253_CDnegMCL_991_log_signal.txt 719,686 Aug. 5, 2004 1254 Table_1254_FH_2043_log_signal.txt 719,919 Aug. 5, 2004 1255 Table_1255_FH_2045_log_signal.txt 719,763 Aug. 5, 2004 1256 Table_1256_FH_2047_log_signal.txt 719,878 Aug. 5, 2004 1257 Table_1257_FH_2120_log_signal.txt 719,940 Aug. 5, 2004 1258 Table_1258_FH_2123_log_signal.txt 719,708 Aug. 5, 2004 1259 Table_1259_FH_2124_log_signal.txt 719,815 Aug. 5, 2004 1260 Table_1260_FH_2138_log_signal.txt 719,598 Aug. 5, 2004 1261 Table_1261_FH_2139_log_signal.txt 719,714 Aug. 5, 2004 1262 Table_1262_FH_2140_log_signal.txt 719,841 Aug. 5, 2004 1263 Table_1263_FH_2141_log_signal.txt 719,943 Aug. 5, 2004 1264 Table_1264_FH_2142_log_signal.txt 720,060 Aug. 5, 2004 1265 Table_1265_FH_2143_log_signal.txt 719,848 Aug. 5, 2004 1266 Table_1266_FH_2159_log_signal.txt 719,736 Aug. 5, 2004 1267 Table_1267_FH_2160_log_signal.txt 719,593 Aug. 5, 2004 1268 Table_1268_FH_2161_log_signal.txt 719,835 Aug. 5, 2004 1269 Table_1269_FH_2162_log_signal.txt 719,992 Aug. 5, 2004 1270 Table_1270_FH_2164_log_signal.txt 719,854 Aug. 5, 2004 1271 Table_1271_FH_2167_log_signal.txt 719,840 Aug. 5, 2004 1272 Table_1272_FL_1073_log_signal.txt 720,209 Aug. 5, 2004 1273 Table_1273_FL_1074_log_signal.txt 719,873 Aug. 5, 2004 1274 Table_1274_FL_1075_log_signal.txt 719,955 Aug. 5, 2004 1275 Table_1275_FL_1076_log_signal.txt 719,619 Aug. 5, 2004 1276 Table_1276_FL_1077_log_signal.txt 720,121 Aug. 5, 2004 1277 Table_1277_FL_1078_log_signal.txt 719,889 Aug. 5, 2004 1278 Table_1278_FL_1080_log_signal.txt 719,874 Aug. 5, 2004 1279 Table_1279_FL_1081_log_signal.txt 720,141 Aug. 5, 2004 1280 Table_1280_FL_1083_log_signal.txt 719,878 Aug. 5, 2004 1281 Table_1281_FL_1085_log_signal.txt 719,880 Aug. 5, 2004 1282 Table_1282_FL_1088_log_signal.txt 720,121 Aug. 5, 2004 1283 Table_1283_FL_1087_log_signal.txt 719,871 Aug. 5, 2004 1284 Table_1284_FL_1088_log_signal.txt 720,091 Aug. 5, 2004 1285 Table_1285_FL_1089_log_signal.txt 719,828 Aug. 5, 2004 1286 Table_1286_FL_1090_log_signal.txt 719,732 Aug. 5, 2004 1287 Table_1287_FL_1097_log_signal.txt 720,055 Aug. 5, 2004 1288 Table_1288_FL_1098_log_signal.txt 719,840 Aug. 5, 2004 1289 Table_1289_FL_1099_log_signal.txt 719,977 Aug. 5, 2004 1290 Table_1290_FL_1102_log_signal.txt 719,965 Aug. 5, 2004 1291 Table_1291_FL_1104_log_signal.txt 719,937 Aug. 5, 2004 1292 Table_1292_FL_1106_log_signal.txt 720,129 Aug. 5, 2004 1293 Table_1293_FL_1107_log_signal.txt 719,953 Aug. 5, 2004 1294 Table_1294_FL_1183_log_signal.txt 719,964 Aug. 5, 2004 1295 Table_1295_FL_1184_log_signal.txt 720,112 Aug. 5, 2004 1296 Table_1296_FL_1185_log_signal.txt 719,953 Aug. 5, 2004 1297 Table_1297_FL_1186_log_signal.txt 720,207 Aug. 5, 2004 1298 Table_1298_FL_1416_log_signal.txt 720,101 Aug. 5, 2004 1299 Table_1299_FL_1417_log_signal.txt 719,952 Aug. 5, 2004 1300 Table_1300_FL_1418_log_signal.txt 719,997 Aug. 5, 2004 1301 Table_1301_FL_1419_log_signal.txt 720,048 Aug. 5, 2004 1302 Table_1302_FL_1422_log_signal.txt 720,019 Aug. 5, 2004 1303 Table_1303_FL_1425_log_signal.txt 719,916 Aug. 5, 2004 1304 Table_1304_FL_1426_log_signal.txt 719,932 Aug. 5, 2004 1305 Table_1305_FL_1427_log_signal.txt 719,845 Aug. 5, 2004 1306 Table_1306_FL_1428_log_signal.txt 720,012 Aug. 5, 2004 1307 Table_1307_FL_1429_log_signal.txt 719,987 Aug. 5, 2004 1308 Table_1308_FL_1432_log_signal.txt 719,795 Aug. 5, 2004 1309 Table_1309_FL_1436_log_signal.txt 720,028 Aug. 5, 2004 1310 Table_1310_FL_1440_log_signal.txt 720,006 Aug. 5, 2004 1311 Table_1311_FL_1445_log_signal.txt 719,751 Aug. 5, 2004 1312 Table_1312_FL_1450_log_signal.txt 719,861 Aug. 5, 2004 1313 Table_1313_FL_1472_log_signal.txt 719,814 Aug. 5, 2004 1314 Table_1314_FL_1473_log_signal.txt 720,151 Aug. 5, 2004 1315 Table_1315_FL_1474_log_signal.txt 720,027 Aug. 5, 2004 1316 Table_1316_FL_1476_log_signal.txt 720,008 Aug. 5, 2004 1317 Table_1317_FL_1477_log_signal.txt 719,820 Aug. 5, 2004 1318 Table_1318_FL_1478_log_signal.txt 719,706 Aug. 5, 2004 1319 Table_1319_FL_1479_log_signal.txt 719,857 Aug. 5, 2004 1320 Table_1320_FL_1480_log_signal.txt 719,713 Aug. 5, 2004 1321 Table_1321_FL_1579_log_signal.txt 719,886 Aug. 5, 2004 1322 Table_1322_FL_1580_log_signal.txt 720,037 Aug. 5, 2004 1323 Table_1323_FL_1581_log_signal.txt 719,898 Aug. 5, 2004 1324 Table_1324_FL_1582_log_signal.txt 720,070 Aug. 5, 2004 1325 Table_1325_FL_1583_log_signal.txt 719,968 Aug. 5, 2004 1325 Table_1326_FL_1584_log_signal.txt 719,970 Aug. 5, 2004 1327 Table_1327_FL_1585_log_signal.txt 720,077 Aug. 5, 2004 1328 Table_1328_FL_1586_log_signal.txt 719,908 Aug. 5, 2004 1329 Table_1329_FL_1588_log_signal.txt 719,859 Aug. 5, 2004 1330 Table_1330_FL_1589_log_signal.txt 720,122 Aug. 5, 2004 1331 Table_1331_FL_1591_log_signal.txt 719,928 Aug. 5, 2004 1332 Table_1332_FL_1594_log_signal.txt 719,773 Aug. 5, 2004 1333 Table_1333_FL_1595_log_signal.txt 720,045 Aug. 5, 2004 1334 Table_1334_FL_1598_log_signal.txt 720,003 Aug. 5, 2004 1335 Table_1335_FL_1599_log_signal.txt 720,156 Aug. 5, 2004 1336 Table_1336_FL_1603_log_signal.txt 720,165 Aug. 5, 2004 1337 Table_1337_FL_1604_log_signal.txt 719,845 Aug. 5, 2004 1338 Table_1338_FL_1606_log_signal.txt 720,024 Aug. 5, 2004 1339 Table_1339_FL_1607_log_signal.txt 719,923 Aug. 5, 2004 1340 Table_1340_FL_1608_log_signal.txt 719,907 Aug. 5, 2004 1341 Table_1341_FL_1610_log_signal.txt 719,954 Aug. 5, 2004 1342 Table_1342_FL_1611_log_signal.txt 719,961 Aug. 5, 2004 1343 Table_1343_FL_1616_log_signal.txt 719,081 Aug. 5, 2004 1344 Table_1344_FL_1617_log_signal.txt 719,893 Aug. 5, 2004 1345 Table_1345_FL_1619_log_signal.txt 719,886 Aug. 5, 2004 1346 Table_1346_FL_1620_log_signal.txt 719,913 Aug. 5, 2004 1347 Table_1347_FL_1622_log_signal.txt 719,925 Aug. 5, 2004 1348 Table_1348_FL_1623_log_signal.txt 720,149 Aug. 5, 2004 1349 Table_1349_FL_1624_log_signal.txt 719,971 Aug. 5, 2004 1350 Table_1350_FL_1625_log_signal.txt 719,688 Aug. 5, 2004 1351 Table_1351_FL_1626_log_signal.txt 719,891 Aug. 5, 2004 1352 Table_1352_FL_1627_log_signal.txt 719,949 Aug. 5, 2004 1353 Table_1353_FL_1628_log_signal.txt 719,751 Aug. 5, 2004 1354 Table_1354_FL_1637_log_signal.txt 719,870 Aug. 5, 2004 1355 Table_1355_FL_1638_log_signal.txt 720,010 Aug. 5, 2004 1356 Table_1356_FL_1639_log_signal.txt 719,825 Aug. 5, 2004 1357 Table_1357_FL_1643_log_signal.txt 719,945 Aug. 5, 2004 1358 Table_1358_FL_1644_log_signal.txt 719,707 Aug. 5, 2004 1359 Table_1359_FL_1645_log_signal.txt 719,953 Aug. 5, 2004 1360 Table_1360_FL_1646_log_signal.txt 719,942 Aug. 5, 2004 1361 Table_1361_FL_1647_log_signal.txt 719,782 Aug. 5, 2004 1362 Table_1362_FL_1648_log_signal.txt 719,810 Aug. 5, 2004 1363 Table_1363_FL_1652_log_signal.txt 719,812 Aug. 5, 2004 1364 Table_1364_FL_1654_log_signal.txt 719,608 Aug. 5, 2004 1365 Table_1365_FL_1655_log_signal.txt 719,993 Aug. 5, 2004 1366 Table_1366_FL_1656_log_signal.txt 710,658 Aug. 5, 2004 1367 Table_1367_FL_1657_log_signal.txt 719,908 Aug. 5, 2004 1368 Table_1368_FL_1660_log_signal.txt 720,041 Aug. 5, 2004 1369 Table_1369_FL_1661_log_signal.txt 720,024 Aug. 5, 2004 1370 Table_1370_FL_1662_log_signal.txt 719,835 Aug. 5, 2004 1371 Table_1371_FL_1664_log_signal.txt 719,887 Aug. 5, 2004 1372 Table_1372_FL_1669_log_signal.txt 719,806 Aug. 5, 2004 1373 Table_1373_FL_1670_log_signal.txt 719,796 Aug. 5, 2004 1374 Table_1374_FL_1675_log_signal.txt 719,960 Aug. 5, 2004 1375 Table_1375_FL_1681_log_signal.txt 720,066 Aug. 5, 2004 1376 Table_1376_FL_1683_log_signal.txt 719,956 Aug. 5, 2004 1377 Table_1377_FL_1684_log_signal.txt 719,691 Aug. 5, 2004 1378 Table_1378_FL_1716_log_signal.txt 719,855 Aug. 5, 2004 1379 Table_1379_FL_1717_log_signal.txt 719,963 Aug. 5, 2004 1380 Table_1380_FL_1718_log_signal.txt 720,047 Aug. 5, 2004 1381 Table_1381_FL_1719_log_signal.txt 719,885 Aug. 5, 2004 1382 Table_1382_FL_1720_log_signal.txt 719,792 Aug. 5, 2004 1383 Table_1383_FL_1729_log_signal.txt 720,000 Aug. 5, 2004 1384 Table_1384_FL_1732_log_signal.txt 719,633 Aug. 5, 2004 1385 Table_1385_FL_l73S_log_signal.txt 719,887 Aug. 5, 2004 1386 Table_1386_FL_1761_log_signal.txt 719,866 Aug. 5, 2004 1387 Table_1387_FL_1764_log_signal.txt 720,078 Aug. 5, 2004 1388 Table_1388_FL_1768_log_signal.txt 720,029 Aug. 5, 2004 1389 Table_1389_FL_1771_log_signal.txt 719,929 Aug. 5, 2004 1390 Table_1390_FL_1772_log_signal.txt 719,921 Aug, 5, 2004 1391 Table_1391_FL_1788_log_signal.txt 719,801 Aug. 5, 2004 1392 Table_1392_FL_1790_log_signal.txt 720,131 Aug. 5, 2004 1393 Table_1393_FL_1792_log_signal.txt 720,113 Aug. 5, 2004 1394 Table_1394_FL_1795_log_signal.txt 720,070 Aug. 5, 2004 1395 Table_1395_FL_1797_log_signal.txt 719,894 Aug. 5, 2004 1396 Table_1396_FL_1799_log_signal.txt 719,604 Aug. 5, 2004 1397 Table_1397_FL_1810_log_signal.txt 719,706 Aug. 5, 2004 1398 Table_1398_FL_1811_log_signal.txt 719,977 Aug. 5, 2004 1399 Table_1399_FL_1825_log_signal.txt 719,804 Aug. 5, 2004 1400 Table_1400_FL_1827_log_signal.txt 720,030 Aug. 5, 2004 1401 Table_1401_FL_1828_log_signal.txt 719,848 Aug. 5, 2004 1402 Table_1402_FL_1829_log_signal.txt 720,004 Aug. 5, 2004 1403 Table_1403_FL_1830_log_signal.txt 719,927 Aug. 5, 2004 1404 Table_1404_FL_1833_log_signal.txt 719,917 Aug. 5, 2004 1405 Table_1405_FL_1834_log_signal.txt 719,704 Aug. 5, 2004 1406 Table_1406_FL_1835_log_signal.txt 720,043 Aug. 5, 2004 1407 Table_1407_FL_1836_log_signal.txt 719,753 Aug. 5, 2004 1408 Table_1408_FL_1837_log_signal.txt 719,848 Aug. 5, 2004 1409 Table_1409_FL_1838_log_signal.txt 719,787 Aug. 5, 2004 1410 Table_1410_FL_1839_log_signal.txt 719,992 Aug. 5, 2004 1411 Table_1411_FL_1841_log_signal.txt 719,730 Aug. 5, 2004 1412 Table_1412_FL_1842_log_signal.txt 719,710 Aug. 5, 2004 1413 Table_1413_FL_1844_log_signal.txt 719,997 Aug. 5, 2004 1414 Table_1414_FL_1845_log_signal.txt 720,016 Aug. 5, 2004 1415 Table_1415_FL_1846_log_signal.txt 719,892 Aug. 5, 2004 1416 Table_1416_FL_1848_log_signal.txt 719,625 Aug. 5, 2004 1417 Table_1417_FL_1850_log_signal.txt 720,047 Aug. 5, 2004 1418 Table_1418_FL_1851_log_signal.txt 719,908 Aug. 5, 2004 1419 Table_1419_FL_1853_log_signal.txt 719,968 Aug. 5, 2004 1420 Table_1420_FL_1854_log_signal.txt 720,215 Aug. 5, 2004 1421 Table_1421_FL_1855_log_signal.txt 719,792 Aug. 5, 2004 1422 Table_1422_FL_1857_log_signal.txt 719,977 Aug. 5, 2004 1423 Table_1423_FL_1861_log_signal.txt 719,995 Aug. 5, 2004 1424 Table_1424_FL_1862_log_signal.txt 719,838 Aug. 5, 2004 1425 Table_1425_FL_1863_log_signal.txt 719,960 Aug. 5, 2004 1426 Table_1426_FL_1864_log_signal.txt 720,101 Aug. 5, 2004 1427 Talee_1427_FL_1866_log_signal.txt 719,826 Aug. 5, 2004 1428 Table_1428_FL_1870_log_signal.txt 719,906 Aug. 5, 2004 1429 Table_1429_FL_1873_log_signal.txt 719,826 Aug. 5, 2004 1430 Table_1430_FL_1874_log_signal.txt 719,987 Aug. 5, 2004 1431 Table_1431_FL_1876_log_signal.txt 719,955 Aug. 5, 2004 1432 Table_1432_FL_1879_log_signal.txt 720,033 Aug. 5, 2004 1433 Table_1433_FL_1880_log_signal.txt 719,838 Aug. 5, 2004 1434 Table_1434_FL_1382_log_signal.txt 719,856 Aug. 5, 2004 1435 Table_1435_FL_1884_log_signal.txt 720,054 Aug. 5, 2004 1436 Table_1436_FL_1885_log_signal.txt 720,091 Aug. 5, 2004 1437 Table_1437_FL_1887_log_signal.txt 719,999 Aug. 5, 2004 1438 Table_1438_FL_1888_log_signal.txt 719,870 Aug. 5, 2004 1439 Table_1439_FL_1890_log_signal.txt 719,775 Aug. 5, 2004 1440 Table_1440_FL_1894_log_signal.txt 719,951 Aug. 5, 2004 1441 Table_1441_FL_1896_log_signal.txt 719,806 Aug. 5, 2004 1442 Table_1442_FL_1897_log_signal.txt 719,814 Aug. 5, 2004 1443 Table_1443_FL_1898_log_signal.txt 719,896 Aug. 5, 2004 1444 Table_1444_FL_1900_log_signal.txt 719,881 Aug. 5, 2004 1445 Table_1445_FL_1903_log_signal.txt 719,743 Aug. 5, 2004 1446 Table_1446_FL_1904_log_signal.txt 719,937 Aug. 5, 2004 1447 Table_1447_FL_1905_log_signal.txt 720,014 Aug. 5, 2004 1448 Table_1448_FL_1906_log_signal.txt 719,525 Aug. 5, 2004 1449 Table_1449_FL_1907_log_signal.txt 719,495 Aug. 5, 2004 1450 Table_1450_FL_1910_log_signal.txt 719,730 Aug. 5, 2004 1451 Table_1451_FL_1912_log_signal.txt 720,188 Aug. 5, 2004 1452 Table_1452_FL_1913_log_signal.txt 719,864 Aug. 5, 2004 1453 Table_1453_FL_1916_log_signal.txt 719,705 Aug. 5, 2004 1454 Table_1454_FL_1918_log_signal.txt 719,919 Aug. 5, 2004 1455 Table_1455_FL_1919_log_signal.txt 719,819 Aug. 5, 2004 1456 Table_1456_FL_735_log_signal.txt 719,892 Aug. 5, 2004 1457 Table_1457_FL_738_log_signal.txt 719,735 Aug. 5, 2004 1458 Table_1458_FL_739_log_signal.txt 719,983 Aug. 5, 2004 1459 Table_1459_FL_878_log_signal.txt 719,976 Aug. 5, 2004 1460 Table_1460_FL_879_log_signal.txt 719,738 Aug. 5, 2004 1461 Table_1461_FL_886_log_signal.txt 719,991 Aug. 5, 2004 1462 Table_1462_FL_888_log_signal.txt 719,876 Aug. 5, 2004 1463 Table_1463_GCB_1005_log_signal.txt 719,924 Aug. 5, 2004 1464 Table_1464_GCB_1008_log_signal.txt 719,529 Aug. 5, 2004 1465 Table_1465_GCB_1009_log_signal.txt 719,942 Aug. 5, 2004 1466 Table_1466_GCB_1021_log_signal.txt 719,714 Aug. 5, 2004 1467 Table_1467_GCB_1025_log_signal.txt 719,756 Aug. 5, 2004 1468 Table_1468_GCB_1026_log_signal.txt 719,819 Aug. 5, 2004 1469 Table_1469_GCB_1037_log_signal.txt 719,756 Aug. 5, 2004 1470 Table_1470_GCB_1039_log_signal.txt 719,940 Aug. 5, 2004 1471 Table_1471_GCB_1049_log_signal.txt 719,794 Aug. 5, 2004 1472 Table_1472_GCB_1051_log_signal.txt 719,894 Aug. 5, 2004 1473 Table_1473_GCB_1058_log_signal.txt 719,709 Aug. 5, 2004 1474 Table_1474_GCB_1060_log_signal.txt 719,801 Aug. 5, 2004 1475 Table_1475_GCB_1990_log_signal.txt 719,399 Aug. 5, 2004 1476 Table_1476_GCB_1991_log_signal.txt 719,470 Aug. 5, 2004 1477 Table_1477_GCB_2017_log_signal.txt 719,443 Aug. 5, 2004 1478 Table_1478_GCB_2018_log_signal.txt 719,412 Aug. 5, 2004 1479 Table_1479_GCB_2095_log_signal.txt 720,035 Aug. 5, 2004 1480 Table_1480_GCB_412_log_signal.txt 719,843 Aug. 5, 2004 Disc 21 of 22 1481 Table_1481_GCB_415_log_signal.txt 719,852 Aug. 5, 2004 1482 Table_1482_GCB_421_log_signal.txt 719,661 Aug. 5, 2004 1483 Table_1483_GCB_424_log_signal.txt 719,871 Aug. 5, 2004 1484 Table_1484_GCB_433_log_signal.txt 719,698 Aug. 5, 2004 1485 Table_1485_GCB_434_log_signal.txt 719,894 Aug. 5, 2004 1486 Table_1486_GCB_438_log_signal.txt 719,648 Aug. 5, 2004 1487 Table_1487_GCB_439_log_signal.txt 719,722 Aug. 5, 2004 1488 Table_1488_GCB_459_log_signal.txt 719,742 Aug. 5, 2004 1489 Table_1489_GCB_470_log_signal.txt 719,565 Aug. 5, 2004 1490 Table_1490_GCB_479_log_signal.txt 719,713 Aug. 5, 2004 1491 Table_1491_GCB_492_log_signal.txt 719,491 Aug. 5, 2004 1492 Tabl8_1492_GCB_517_log_signal.txt 719,595 Aug. 5, 2004 1493 Table_1493_GCB_523_log_signal.txt 719,784 Aug. 5, 2004 1494 Table_1494_GCB_524_log_signal.txt 719,991 Aug. 5, 2004 1495 Table_1495_GCB_529_log_signal.txt 719,591 Aug. 5, 2004 1498 Table_1496_GCB_533_log_signal.txt 719,489 Aug. 5, 2004 1497 Table_1497_GCB_537_log_signal.txt 719,866 Aug. 5, 2004 1498 Table_1498_GCB_543_log_signal.txt 719,868 Aug. 5, 2004 1499 Table_1499_GCB_545_log_signal.txt 719,723 Aug. 5, 2004 1500 Table_1500_GCB_549_log_signal.txt 719,961 Aug. 5, 2004 1501 Table_1501_GCB_550_log_signal,txt 719,631 Aug. 5, 2004 1502 Table_1502_GCB_553_log_signal.txt 719,658 Aug. 5, 2004 1503 Table_1503_GCB_565_log_signal.txt 719,779 Aug. 5, 2004 1504 Table_1504_GCB_572_log_signal.txt 719,880 Aug. 5, 2004 1505 Table_1505_GCB_817_log_signal.txt 719,961 Aug. 5, 2004 1506 Table_1506_GCB_818_log_signal.txt 719,784 Aug. 5, 2004 1507 Table_1507_GCB_619_log_signal.txt 719,917 Aug. 5, 2004 1508 Table_1508_GCB_623_log_signal.txt 719,983 Aug. 5, 2004 1509 Table_1509_GCB_627_log_signal.txt 719,791 Aug. 5, 2004 1510 Table_1510_GCB_654_log_signal.txt 719,680 Aug. 5, 2004 1511 Table_1511_GCB_661_log_signal.txt 719,750 Aug. 5, 2004 1512 Table_1512_GCB_689_log_signal.txt 719,804 Aug. 5, 2004 1513 Table_1513_GCB_672_log_signal.txt 719,690 Aug. 5, 2004 1514 Table_1514_GCB_674_log_signal.txt 719,705 Aug. 5, 2004 1515 Table_1515_GCB_675_log_signal.txt 719,403 Aug. 5, 2004 1516 Table_1516_GCB_681_log_signal.txt 719,662 Aug. 5, 2004 1517 Table_1517_GCB_688_log_signal.txt 719,901 Aug. 5, 2004 1518 Table_1518_GCB_695_log_signal.txt 719,669 Aug. 5, 2004 1519 Table_1519_GCB_698_log_signal.txt 719,863 Aug. 5, 2004 1520 Table_1520_GCB_701_log_signal.txt 719,936 Aug. 5, 2004 1521 Table_1521_GCB_710_log_signal.txt 719,846 Aug. 5, 2004 1522 Table_1522_GCB_711_log_signal.txt 719,611 Aug. 5, 2004 1523 Table_1523_GCB_722_log_signal.txt 719,778 Aug. 5, 2004 1524 Table_1524_GCB_724_log_signal.txt 719,711 Aug. 5, 2004 1525 Table_1525_GCB_731_log_signal.txt 719,984 Aug. 5, 2004 1526 Table_1526_GCB_742_log_signal.txt 719,996 Aug. 5, 2004 1527 Table_1527_GCB_744_log_signal.txt 719,908 Aug. 5, 2004 1528 Table_1528_GCB_745_log_signal.txt 719,873 Aug. 5, 2004 1529 Table_1529_GCB_747_log_signal.txt 719,969 Aug. 5, 2004 1530 Table_1530_GCB_749_log_signal.txt 719,685 Aug. 5, 2004 1531 Table_1531_GCB_758_log_signal.txt 719,776 Aug. 5, 2004 1532 Table_1532_GCB_772_log_signal.txt 720,003 Aug. 5, 2004 1533 Table_1533_GCB_777_log_signal.txt 719,908 Aug. 5, 2004 1534 Table_1534_GCB_792_log_signal.txt 719,971 Aug. 5, 2004 1535 Table_1535_GCB_795_log_signal.txt 719,759 Aug. 5, 2004 1536 Table_1536_GCB_797_log_signal.txt 719,760 Aug. 6, 2004 1537 Table_1537_GCB_803_log_signal.txt 719,974 Aug. 6, 2004 1538 Table_1538_GCB_810_log_signal.txt 719,852 Aug. 5, 2004 1539 Table_1539_GCB_817_log_signal.txt 719,764 Aug. 5, 2004 1540 Table_1540_GCB_818_log_signal.txt 719,583 Aug. 5, 2004 1541 Table_1541_GCB_819_log_signal.txt 719,786 Aug. 5, 2004 1542 Table_1542_GCB_821_log_signal.txt 719,744 Aug. 5, 2004 1543 Table_1543_GCB_832_log_signal.txt 719,702 Aug. 5, 2004 1544 Table_1544_GCB_836_log_signal.txt 719,639 Aug. 5, 2004 1545 Table_1545_GCB_840_log_signal.txt 720,192 Aug. 5, 2004 1546 Table_1546_GCB_847_log_signal.txt 719,894 Aug. 5, 2004 1547 Table_1547_GCB_860_log_signal.txt 719,781 Aug. 5, 2004 1548 Table_1548_GCB_871_log_signal.txt 719,867 Aug. 5, 2004 1549 Table_1549_GCB_874_log_signal.txt 719,863 Aug. 5, 2004 1550 Table_1550_GCB_995_log_signal.txt 719,901 Aug. 5, 2004 1551 Table_1551_LPC_2224_log_signal.txt 719,928 Aug. 5, 2004 1552 Table_1552_LPC_2232_log_signal.txt 720,084 Aug. 5, 2004 1553 Table_1553_LPC_2261_log_signal.txt 719,850 Aug. 5, 2004 1554 Table_1554_LPC_2262_log_signal.txt 720,236 Aug. 5, 2004 1555 Table_1555_LPC_2263_log_signal.txt 720,029 Aug. 5, 2004 1556 Table_1556_LPC_2265_log_signal.txt 719,906 Aug. 5, 2004 1557 Table_1557_Lymbl_2185_log_signal.txt 719,715 Aug. 5, 2004 1558 Table_1558_Lymbl_2186_log_signal.txt 719,629 Aug. 5, 2004 1559 Table_1559_Lymbl_2249_log_signal.txt 720,127 Aug. 5, 2004 1560 Table_1560_MALT_Gastric_2021_log_signal.txt 719,609 Aug. 5, 2004 1561 Table_1561_MALT_Gastric_2041_log_signal.txt 719,769 Aug. 5, 2004 1562 Table_1562_MALT_Gastric_2065_log_signal.txt 719,679 Aug. 5, 2004 1563 Table_1563_MALT_Gastric_2067_log_signal.txt 719,927 Aug. 5, 2004 1564 Table_1564_MALT_Gastric_2070_log_signal.txt 719,871 Aug. 5, 2004 1565 Table_1565_MALT_Gastric_2110_log_signal.txt 719,932 Aug. 5, 2004 1566 Table_1566_MALT_Gastric_2111_log_signal.txt 719,305 Aug. 5, 2004 1567 Table_1567_MALT_Gastric_2112_log_signal.txt 719,897 Aug. 5, 2004 1568 Table_1568_MALT_Gastric_2270_log_signal.txt 719,916 Aug. 5, 2004 1569 Table_1569_MALT_lung_2202_log_signal.txt 720,174 Aug. 5, 2004 1570 Table_1570_MALT_salivary_2243_log_signal.txt 720,111 Aug. 5, 2004 1571 Table_1571_MALT_tonsil_2211_log_signal.txt 720,102 Aug. 5, 2004 1572 Table_1572_MALT_unk_2178_log_signal.txt 719,940 Aug. 5, 2004 1573 Table_1573_MALT_unk_2182_log_signal.txt 719,681 Aug. 5, 2004 1574 Table_1574_MALT_unk_2183_log_signal.txt 720,031 Aug. 5, 2004 1575 Table_1575_MALT_unk_2184_log_signal.txt 720,023 Aug. 5, 2004 1576 Table_1576_MCL_1012_log_signal.txt 719,967 Aug. 5, 2004 1577 Table_1577_MCL_1091_log_signal.txt 719,861 Aug. 5, 2004 1578 Table_1578_MCL_1114_log_signal.txt 720,107 Aug. 5, 2004 1579 Table_1579_MCL_1128_log_sighal.txt 719,887 Aug. 5, 2004 1580 Table_1580_MCL_1150_log_signal.txt 719,990 Aug. 5, 2004 1581 Table_1581_MCL_1162_log_signal.txt 719,887 Aug. 5, 2004 1582 Table_1582_MCL_1166_log_signal.txt 719,942 Aug. 5, 2004 1583 Table_1583_MCL_1194_log_signal.txt 719,765 Aug. 5, 2004 1584 Table_1584_MCL_885_log_signal.txt 719,947 Aug. 5, 2004 1585 Table_1585_MCL_918_log_signal.txt 719,871 Aug. 5, 2004 1586 Table_1586_MCL_924_log_signal.txt 729,318 Aug. 5, 2004 1587 Table_1587_MCL_925_log_signal.txt 719,991 Aug. 5, 2004 1588 Table_1588_MCL_926_log_signal.txt 720,109 Aug. 5, 2004 1589 Table_1589_MCL_936_log_signal.txt 720,025 Aug. 5, 2004 1590 Table_1590_MCL_939_log_signal.txt 720,025 Aug. 5, 2004 1591 Table_1591_MCL_953_log_signal.txt 719,562 Aug. 5, 2004 1592 Table_1592_MCL_956_log_signal.txt 719,923 Aug. 5, 2004 1593 Table_1593_MCL_964_log_signal.txt 719,968 Aug. 5, 2004 1594 Table_1594_MCL_966_log_signal.txt 719,679 Aug. 5, 2004 1595 Table_1595_MCL_968_log_signal.txt 719,778 Aug. 5, 2004 1596 Table_1596_MCL_970_log_signal.txt 719,737 Aug. 5, 2004 1597 Table_1597_Mult_Myeloma_H1112_100001_log_signal.txt 719,421 Aug. 5, 2004 1598 Table_1598_Mult_Myeloma_HDLM2_100002_log_signal.txt 719,052 Aug. 5, 2004 1599 Table_1599_Mult_Myeloma_JIM3_100003_log_signal.txt 719,345 Aug. 5, 2004 1600 Table_1600_Mult_Myeloma_JJN3_100004_log_signal.txt 719,543 Aug. 5, 2004 1601 Table_1601_Mult_Myeloma_KMS11_100005_log_signal.txt 719,485 Aug. 5, 2004 1602 Table_1602_Mult_Myeloma_KMS12_100006_log_signal.txt 719,226 Aug. 5, 2004 1603 Table_1603_Mult_Myeloma_LB84_1_100007_log_signal.txt 719,446 Aug. 5, 2004 1604 Table_1604_Mult_Myeloma_LP1_100008_log_signal.txt 719,215 Aug. 5, 2004 1605 Table_1505_Mult_Myeloma_U266_100009_log_signal.txt 719,709 Aug. 5, 2004 1606 Table_1606_NMZ_2231_log_signal.txt 720,065 Aug. 5, 2004 1607 Table_1607_NMZ_2242_log_signal.txt 719,936 Aug. 5, 2004 1608 Table_1608_NMZ_2254_log_signal.txt 719,889 Aug. 5, 2004 1609 Table_1609_PMBL_1006_log_signal.txt 719,569 Aug. 5, 2004 1610 Table_1610_PMBL_1024_log_signal.txt 719,689 Aug. 5, 2004 1611 Table_1611_PMBL_1048_log_signal.txt 719,535 Aug. 5, 2004 1612 Table_1612_PMBL_1053_log_signal.txt 719,833 Aug. 5, 2004 1613 Table_1613_PMBL_1920_log_signal.txt 719,681 Aug. 5, 2004 1614 Table_1614_PMBL_1921_log_signal.txt 719,749 Aug. 5, 2004 1615 Table_1615_PMBL_1923_log_signal.txt 719,681 Aug. 5, 2004 1616 Table_1616_PMBL_1924_log_signal.txt 719,659 Aug. 5, 2004 1617 Table_1617_PMBL_1935_log_signal.txt 719,716 Aug. 5, 2004 1618 Table_1618_PMBL_1941_log_signal.txt 719,793 Aug. 5, 2004 1619 Table_1619_PMBL_1942_log_signal.txt 719,S57 Aug. 5, 2004 1620 Table_1620_PMBL_1943_log_signal.txt 719,523 Aug. 5, 2004 1621 Table_1621_PMBL_1945_log_signal.txt 719,389 Aug. 5, 2004 1622 Table_1622_PMBL_1948_log_signal.txt 719,581 Aug. 5, 2004 1623 Table_1623_PMBL_1949_log_signal.txt 719,805 Aug. 5, 2004 1624 Table_1624_PMBL_1989_log_signal.txt 719,642 Aug. 5, 2004 1625 Table_1625_PMBL_1992_log_signal.txt 719,564 Aug. 5, 2004 1626 Table_1626_PMBL_1993_log_signal.txt 719,621 Aug. 5, 2004 1627 Table_1627_PMBL_2002_log_signal.txt 719,718 Aug. 5, 2004 1628 Table_1628_PMBL_2019_log_signal.txt 719,547 Aug. 5, 2004 1629 Table_1629_PMBL_2020_log_signal.txt 719,620 Aug. 5, 2004 1630 Table_1630_PMBL_2092_log_signal.txt 719,844 Aug. 5, 2004 1631 Table_1631_PMBL_484_log_signal.txt 719,920 Aug. 5, 2004 1632 Table_1632_PMBL_546_log_signal.txt 720,204 Aug. 5, 2004 1683 Table_1633_PMBL_570_log_signal.txt 719,670 Aug. 5, 2004 1634 Table_1634_PMBL_621_log_signal.txt 719,915 Aug. 5, 2004 1635 Table_1635_PMBL_638_log_signal.txt 719,798 Aug. 5, 2004 1636 Table_1636_PMBL_691_log_signal.txt 719,763 Aug. 5, 2004 1637 Table_1637_PMBL_791_log_signal.txt 719,762 Aug. 5, 2004 1638 Table_1638_PMBL_824_log_signal.txt 719,876 Aug. 5, 2004 1639 Table_1639_PMBL_906_log_signal.txt 719,758 Aug. 5, 2004 1640 Table_1640_PMBL_994_log_signal.txt 719,755 Aug. 5, 2004 1641 Table_1641_PMBL_998_log_signal.txt 719,786 Aug. 5, 2004 1642 Table_1642_PTLD_1817_log_signal.txt 719,584 Aug. 5, 2004 1643 Tabl8_1643_PTLD_1824_log_signal.txt 719,6Z6 Aug. 5, 2004 1644 Table_1644_PTLD_1981_log_signal.txt 719,522 Aug. 5, 2004 1645 Table_1645_PTLD_1986_log_signal.txt 719,870 Aug. 5, 2004 1646 Table_1646_SLL_1151_log_signal.txt 719,912 Aug. 5, 2004 1647 Table_1647_SLL_1153_log_signal.txt 720,059 Aug. 5, 2004 1648 Table_1648_SLL_1155_log_signal.txt 720,031 Aug. 5, 2004 1649 Table_1649_SLL_1156_log_signal.txt 720,140 Aug. 5, 2004 1650 Table_1650_SLL_1158_log_signal.txt 720,029 Aug. 5, 2004 1651 Table_1651_SLL_1213_log_signal.txt 720,069 Aug. 5, 2004 1652 Table_1652_SLL_1214_log_signal.txt 719,849 Aug. 5, 2004 1653 Table_1653_SLL_1216_log_signal.txt 719,817 Aug. 5, 2004 1654 Table_1654_SLL_1217_log_signal.txt 719,829 Aug. 5, 2004 1655 Table_1655_SLL_1218_log_signal.txt 719,694 Aug. 5, 2004 1656 Table_1656_SLL_1220_log_signal.txt 719,763 Aug. 5, 2004 1657 Table_1657_SLL_1229_log_signal.txt 719,434 Aug. 5, 2004 1658 Table_1658_SLL_1231_log_signal.txt 719,688 Aug. 5, 2004 1659 Table_1659_SLL_1235_log_signal.txt 719,578 Aug. 5, 2004 1660 Table_1660_SLL_1236_log_signal.txt 719,791 Aug. 5, 2004 1661 Table_1661_SLL_1238_log_signal.txt 719,633 Aug. 5, 2004 1662 Table_1662_SLL_1240_log_signal.txt 719,788 Aug. 5, 2004 1663 Table_1663_Splenic_2061_log_signal.txt 719,871 Aug. 5, 2004 1664 Table_1664_Splenic_2074_log_signal.txt 720,044 Aug. 5, 2004 1665 Table_1665_Splenic_2075_log_signal.txt 719,895 Aug. 5, 2004 1666 Table_1666_Splenic_2077_log_signal.txt 720,030 Aug. 5, 2004 1667 Table_1667_Splenic_2079_log_signal.txt 719,789 Aug. 5, 2004 1668 Table_1668_Splenic_2104_log_signal.txt 719,813 Aug. 5, 2004 1669 Table_1669_Splenic_2105_log_signal.txt 720,016 Aug. 5, 2004 1670 Table_1670_Splenic_2106_log_signal.txt 719,757 Aug. 5, 2004 1671 Table_1671_Splenic_2256_log_signal.txt 720,008 Aug. 5, 2004 1672 Table_1672_Splenic_2257_log_signal.txt 719,730 Aug. 5, 2004 1673 Table_1673_Splenic_2258_log_signal.txt 719,797 Aug. 5, 2004 1674 Table_1674_Splenic_2259_log_signal.txt 719,978 Aug. 5, 2004 1675 Table_1675_Splenic_2260_log_signal.txt 719,700 Aug. 5, 2004 1676 Table_1676_UC_DLBCL_1001_log_signal.txt 719,781 Aug. 5, 2004 1677 Table_1677_UC_DLBCL_1004_log_signal.txt 719,748 Aug. 5, 2004 1678 Table_1678_UC_DLBCL_1007_log_signal.txt 719,736 Aug. 5, 2004 1679 Table_1679_UC_DLBCL_1018_log_signal.txt 719,605 Aug. 5, 2004 1680 Table_1680_UC_DLBCL_1041_log_signal.txt 719,657 Aug. 5, 2004 1681 Table_1681_UC_DLBCL_1054_log_signal.txt 719,834 Aug. 5, 2004 1682 Table_1682_UC_DLBCL_306_log_signal.txt 719,532 Aug. 5, 2004 1683 Table_1683_UC_DLBCL_310_log_signal.txt 719,704 Aug. 5, 2004 1684 Table_1684_UC_DLBCL_449_log_signal.txt 719,556 Aug. 5, 2004 1685 Table_1685_UC_DLBCL_452_log_signal.txt 720,038 Aug. 5, 2004 1686 Table_1686_UC_DLBCL_458_log_signal.txt 720,003 Aug. 5, 2004 1687 Table_1687_UC_DLBCL_460_log_signal.txt 719,789 Aug. 5, 2004 1688 Table_1688_UC_DLBCL_491_log_signal.txt 719,734 Aug. 5, 2004 1689 Table_1689_UC_DLBCL_528_log_signal.txt 719,715 Aug. 5, 2004 1690 Table_1690_UC_DLBCL_615_log_signal.txt 719,713 Aug. 5, 2004 1691 Table_1691_UC_DLBCL_625_log_signal.txt 719,795 Aug. 5, 2004 1692 Table_1692_UC_DLBCL_664_log_signal.txt 719,885 Aug. 5, 2004 1693 Table_1693_UC_DLBCL_671_log_signal.txt 719,709 Aug. 5, 2004 1694 Table_1694_UC_DLBCL_682_log_signal.txt 719,847 Aug. 5, 2004 1695 Table_1695_UC_DLBCL_683_log_signal.txt 719,809 Aug. 5, 2004 1696 Table_1696_UC_DLBCL_684_log_signal.txt 719,647 Aug. 5, 2004 1697 Table_1697_UC_DLBCL_748_log_signal.txt 719,827 Aug. 5, 2004 1698 Table_1698_UC_DLBCL_751_log_signal.txt 719,759 Aug. 5, 2004 1699 Table_1699_UC_DLBCL_808_log_signal.txt 719,655 Aug. 5, 2004 1700 Table_1700_UC_DLBCL_831_log_signal.txt 719,881 Aug. 5, 2004 1701 Table_1701_UC_DLBCL_834_log_signal.txt 719,550 Aug. 5, 2004 1702 Table_1702_UC_DLBCL_838_log_signal.txt 719,865 Aug. 5, 2004 1703 Table_1703_UC_DLBCL_851_log_signal.txt 719,904 Aug. 5, 2004 1704 Table_1704_UC_DLBCL_854_log_signal.txt 719,912 Aug. 5, 2004 1705 Table_1705_UC_DLBCL_855_log_signal.txt 719,903 Aug. 5, 2004 1706 Table_1706_UC_DLBCL_856_log_signal.txt 719,736 Aug. 5, 2004 1707 Table_1707_ABC_Stats.txt 19,458666 Aug. 22, 2004 1708 Table_1708_BL_Stats.txt 20,646,028 Aug. 22, 2004 1709 Table_1709_FH_Stats.txt 20,631,528 Aug. 22, 2004 1710 Table_1710_FL_Stats.txt 21,832,450 Aug. 22, 2004 1711 Table_1711_Gastric_MALT_Stats.txt 19,429,850 Aug. 22, 2004 1712 Table_1712_GCB_Stats.txt 19,460,177 Aug. 22, 2004 1713 Table_1713_LPC_Stats.txt 20,569,572 Aug. 22, 2004 1714 Table_1714_Lymbl_Stats.txt 20,624,886 Aug. 22, 2004 1715 Table_1715_MCL_Stats.txt 21,816,811 Aug. 22, 2004 1716 Table_1716_Mult_Myeloma_Stats.txt 20,655,192 Aug. 22, 2004 1717 Table_1717_NMZ_Stats.txt 20,633,800 Aug. 22, 2004 1718 Table_1718_PMBL_Stats.txt 19,448,976 Aug. 22, 2004 1719 Table_1719_PTLD_Stats.txt 20,634,040 Aug. 22, 2004 1720 Table_1720_SLL_Stats.txt 20,625,386 Aug. 22, 2004 1721 Table_1721_Splenic_Stats.txt 20,608,286 Aug. 22, 2004 1722 Table_1722_All_MALT_Stats.txt 19,401341 Aug. 22, 2004 1723 Table_1723_DLBCL_Stats.txt 18,266589 Aug. 22, 2004 1725 Table_1725_BL_2032_52748.txt 182,232 Aug. 21, 2004 1726 Table_1726_BL_2033_52749.txt 182,411 Aug. 21, 2004 1727 Table_1727_BL_2035_52750.txt 182,439 Aug. 21, 2004 1728 Table_1728_BL_2036_52751.txt 182,419 Aug. 21, 2004 1729 Table_1729_BL_2037_53294.txt 182,586 Aug. 21, 2004 1730 Table_1730_BL_2038_52752.txt 182,526 Aug. 21, 2004 1731 Table_1731_BL_2082_53031.txt 182,367 Aug. 23, 2004 1732 Table_1732_BL_2083_53293.txt 182,432 Aug. 23, 2004 1733 Table_1733_BL_2086_52754.txt 182,376 Aug. 23, 2004 1734 Table_1734_BL_2088_52755.txt 182,485 Aug. 23, 2004 1735 Table_1735_BL_2090_52758.txt 182,594 Aug. 23, 2004 1736 Table_1736_BL_2091_52759.txt 182,553 Aug. 23, 2004 1737 Table_1737_BL_2097_52760.txt 182,456 Aug. 23, 2004 1738 Table_1738_BL_2099_52761.txt 182,398 Aug. 23, 2004 1739 Table_1739_BL_2100_52762.txt 182,615 Aug. 23, 2004 1740 Table_1740_BL_2101_52763.txt 132,545 Aug. 23, 2004 1741 Table_1741_BL_2103_52764.txt 182,403 Aug. 23, 2004 1742 Table_1742_BL_2125_52765.txt 182,445 Aug. 23, 2004 1743 Table_1743_BL_2126_52766.txt 182,313 Aug. 23, 2004 1744 Table_1744_BL_2127_52767.txt 182,284 Aug. 23, 2004 1745 Table_1745_BL_2128_52768.txt 182,178 Aug. 23, 2004 1746 Table_1746_BL_2129_52769.txt 182,403 Aug. 23, 2004 1747 Table_1747_BL_2267_52770.txt 182,514 Aug. 23, 2004 1748 Table_1748_BL_2268_52771.txt 182,535 Aug. 23, 2004 1749 Table_1749_BL_2269_52772.txt 182,371 Aug. 23, 2004 1750 Table_1750_BL_2271_52773.txt 182,496 Aug. 23, 2004 1751 Table_1751_BL_2386_53355.txt 182,265 Aug. 23, 2004 1752 Table_1752_BL_2387_53356.txt 182,708 Aug. 24, 2004 1753 Table_1753_BL_2388_53357.txt 182,539 Aug. 24, 2004 1754 Table_1754_BL_2389_53358.txt 182,360 Aug. 24, 2004 1755 Table_1755_BL_2390_53359.txt 182,449 Aug. 25, 2004 1756 Table_1756_BL_2391_53360.txt 182,528 Aug. 25, 2004 1757 Table_1757_BL_2392_53361.txt 182,519 Aug. 25, 2004 1758 Thbie_1758_BL_2393_53362.txt 182,354 Aug. 25, 2004 1759 Table_1759_BL_2394_53363.txt 182,321 Aug. 25, 2004 1760 Table_1760_BL_2395_53364.txt 182,615 Aug. 25, 2004 1761 Table_1761_BL_2896_53365.txt 182,395 Aug. 25, 2004 1762 Table_1762_BL_2400_53366.txt 182,611 Aug. 25, 2004 1763 Table_1763_BL_2402_53367.txt 182,344 Aug. 25, 2004 1764 Table_1764_BL_2405_53368.txt 182,461 Aug. 25, 2004 1765 Table_1765_BL_2406_53369.txt 182,368 Aug. 25, 2004 1766 Table_1766_BL_2409_53370.txt 182,484 Aug. 25, 2004 1767 Table_1767_BL_2417_53377.txt 182,524 Aug. 25, 2004 1768 Table_1768_BL_2418_53378.txt 182,353 Aug. 25, 2004 1769 Table_1769_BL_2419_53379.txt 182,524 Aug. 25, 2004 1770 Table_1770_BL_2420_53380.txt 182,384 Aug. 25, 2004 1771 Table_1771_BL_2421_53381.txt 182,445 Aug. 26, 2004 1772 Table_1772_BL_2422_53382.txt 182,464 Aug. 26, 2004 1923 Table_1923_DLBCL_669_52074.txt 182,457 Aug. 23, 2004 1924 Table_1924_DLBCL_671_52075.txt 182,374 Aug. 23, 2004 1925 Table_1925_DLBCL_672_52076.txt 182,417 Aug. 23, 2004 1926 Table_1926_DLBCL_674_52004.txt 182,482 Aug. 23, 2004 1927 Table_1927_DLBCL_675_52095.txt 182,127 Aug. 23, 2004 1928 Table_1928_DLBCL_676_52977.txt 182,312 Aug. 23, 2004 1929 Table_1929_DLBCL_678_51829.txt 182,308 Aug. 23, 2004 1930 Table_1938_DLBCL_681_52152.txt 182,395 Aug. 23, 2004 1931 Table_1931_DLBCL_882_52154.txt 182,477 Aug. 23, 2004 1932 Table_1932_DLBCL_683_52153.txt 182,390 Aug. 23, 2004 1933 Table_1933_DLBCL_684_52155.txt 182,346 Aug. 23, 2004 1934 Table_1934_DLBCL_687_52077.txt 182,331 Aug. 23, 2004 1935 Table_1935_DLBCL_688_52078.txt 182,333 Aug. 23, 2004 1936 Table_1936_DLBCL_689_52079.txt 182,362 Aug. 23, 2004 1937 Table_1937_DLBCL_692_51825.txt 182,320 Aug. 23, 2004 1938 Table_1938_DLBCL_694_52080.txt 182,236 Aug. 23, 2004 1939 Table_1939_DLBCL_695_52081.txt 182,341 Aug. 23, 2004 1940 Table_1940_DLBCL_698_51828.txt 182,428 Aug. 23, 2004 1941 Table_1941_DLBCL_700_51835.txt 182,292 Aug. 23, 2004 1942 Table_1942_DLBCL_701_52244.txt 182,408 Aug. 23, 2004 1943 Table_1943_DLBCL_702_51834.txt 182,320 Aug. 23, 2004 1944 Table_1944_DLBCL_704_52082.txt 182,198 Aug. 23, 2004 1945 Table_1945_DLBCL_709_52123.txt 182,355 Aug. 23, 2004 1946 Table_1946_DLBCL_710_52122.txt 182,360 Aug. 23, 2004 1947 Table_1947_DLBCL_711_52083.txt 182,453 Aug. 23, 2004 1948 Table_1948_DLBCL_712_52084.txt 182,345 Aug. 23, 2004 1949 Table_1949_DLBCL_714_52125.txt 182,513 Aug. 23, 2004 1950 Table_1950_DLBCL_717_52124.txt 182,376 Aug. 23, 2004 1951 Table_1951_DLBCL_722_52181.txt 182,411 Aug. 23, 2004 1952 Table_1952_DLBCL_724_52978.txt 182,354 Aug. 23, 2004 1953 Table_1953_DLBCL_725_52979.txt 182,349 Aug. 23, 2004 1954 Table_1954_DLBCL_726_52980.txt 182,382 Aug. 23, 2004 1955 Table_1955_DLBCL_730_51833.txt 182,186 Aug. 23, 2004 1956 Table_1956_DLBCL_731_51832.txt 182,453 Aug. 23, 2004 1957 Table_1957_DLBCL_742_52085.txt 182,445 Aug. 23, 2004 1958 Table_1958_DLBCL_744_52086.txt 182,315 Aug. 23, 2004 1959 Table_1959_DLBCL_745_52096.txt 182,461 Aug. 23, 2004 1960 Table_1960_DLBCL_747_52097.txt 182,405 Aug. 23, 2004 1961 Table_1961_DLBCL_748_52245.txt 182,432 Aug. 23, 2004 1962 Table_1962_DLBCL_749_51826.txt 182,374 Aug. 23, 2004 1963 Table_1963_DLBCL_751_52278.txt 182,393 Aug. 23, 2004 1964 Table_1964_DL8CL_753_51824.txt 182,281 Aug. 23, 2004 1965 Table_1965_DLBCL_756_52098.txt 182,335 Aug. 23, 2004 1966 Table_1966_DLBCL_758_51831.txt 182,344 Aug. 23, 2004 1967 Table_1967_DLBCL_771_52983.txt 182,405 Aug. 23, 2004 1968 Table_1968_DLBCL_772_52984.txt 182,523 Aug. 23, 2004 1969 Table_1969_DLBCL_777_51827.txt 182,369 Aug. 23, 2004 1970 Table_1970_DLBCL_779_51822.txt 182,217 Aug. 23, 2004 1971 Table_1971_DLBCL_789_51821.txt 182,375 Aug. 23, 2004 1972 Table_1972_DLBCL_792_51820.txt 182,462 Aug. 23, 2004 1773 Table_1773_BL_2425_53383.txt 182,690 Aug. 25, 2004 1774 Table_1774_BL_2426_53384.txt 182,593 Aug. 25, 2004 1775 Table_1775_BL_2427_53385.txt 182,514 Aug. 25, 2004 1776 Table_1776_BL_2428_53386.txt 182,479 Aug. 25, 2004 1777 Table_1777_BL_2430_53387.txt 182,369 Aug. 25, 2004 1778 Table_1778_BL_2431_53388.txt 182,235 Aug. 25, 2004 1779 Table_1779_BL_2432_53389.txt 182,564 Aug. 25, 2004 1780 Table_1780_BL_2433_53390.txt 182,477 Aug. 25, 2004 1781 Table_1781_BL_2434_53391.txt 182,590 Aug. 25, 2004 1782 Table_1782_BL_2435_53392.txt 182,553 Aug. 25, 2004 1783 Table_1783_BL_2437_53393.txt 182,354 Aug. 26, 2004 1784 Table_1784_BL_2438_53394.txt 182,345 Aug. 26, 2004 1785 Table_1785_CD1N_1013_52338.txt 182,526 Aug. 25, 2004 1786 Table_1756_CD1N_1116_52359.txt 182,611 Aug. 26, 2004 1787 Table_1787_CD1N_1125_52360.txt 182,605 Aug. 26, 2004 1788 Table_1788_CD1N_1265_52792.txt 182,740 Aug. 26, 2004 1789 Table_1789_CD1N_2198_52794.txt 182,606 Aug. 26, 2004 1790 Table_1790_CD1N_2272_52793.txt 182,532 Aug. 26, 2004 1791 Table_1791_CD1N_930_52332.txt 182,670 Aug. 26, 2004 1792 Table_1792_CD1N_950_52333.txt 182,682 Aug. 26, 2004 1793 Table_1793_CD1N_985_52334.txt 182,692 Aug. 26, 2004 1794 Table_1794_CD1N_991_52335.txt 182,668 Aug. 26, 2004 1795 Table_1795_CLL_1275_52361.txt 182,084 Aug. 26, 2004 1796 Table_1796_CLL_1277_52363.txt 182,150 Aug. 26, 2004 1797 Table_1797_CLL_1278_52364.txt 182,109 Aug. 26, 2004 1798 Table_1798_CLL_1279_52365.txt 182,039 Aug. 26, 2004 1799 Table_1799_CLL_1283_52366.txt 182,198 Aug. 26, 2004 1800 Table_1800_CLL_1292_52367.txt 182,099 Aug. 26, 2004 1801 Table_1801_CLL_1293_52368.txt 182,150 Aug. 26, 2004 1802 Table_1802_CLL_1294_52369.txt 182,208 Aug. 26, 2004 1803 Table_1803_CLL_1297_52370.txt 182,142 Aug. 26, 2004 1804 Table_1804_CLL_1298_52371.txt 182,102 Aug. 26, 2004 1805 Table_1805_CLL_1302_52372.txt 182,118 Aug. 26, 2004 1806 Table_1806_CLL_1311_52373.txt 182,138 Aug. 26, 2004 1807 Table_1807_CLL_1344_51718.txt 182,161 Aug. 26, 2004 1808 Table_1808_CLL_1379_51719.txt 182,375 Aug. 26, 2004 1809 Table_1809_CLL_1381_51720.txt 182,238 Aug. 26, 2004 1810 Table_1810_CLL_1394_51721.txt 182,185 Aug. 26, 2004 1811 Table_1811_DLBCL_1000_51981.txt 182,443 Aug. 26, 2004 1812 Table_1812_DLBCL_1001_52158.txt 182,528 Aug. 26, 2004 1813 Table_1813_DLBCL_1002_51697.txt 182,578 Aug. 27, 2004 1814 Table_1814_DLBCL_1004_52159.txt 182,456 Aug. 27, 2004 1815 Table_1815_DLBCL_1005_52160.txt 182,642 Aug. 26, 2004 1816 Table_1816_DLBCL_1006_52161.txt 182,480 Aug. 26, 2004 1817 Table_1817_DLBCL_1007_52162.txt 182,521 Aug. 26, 2004 1818 Table_1818_DLBCL_1008_5a163.txt 182,559 Aug. 26, 2004 1819 Table_1819_DLBCL_1009_52971.txt 182,393 Aug. 26, 2004 1820 Table_1820_DLBCL_1018_52165.txt 182,598 Aug. 27, 2004 1821 Table_1821_DLBCL_1021_52166.txt 182,432 Aug. 27, 2004 1822 Table_1822_DLBCL_1023_52167.txt 182,504 Aug. 27, 2004 1823 Table_1823_DLBCL_1024_52168.txt 182,695 Aug. 27, 2004 1824 Table_1824_DLBCL_1025_52169.txt 182,615 Aug. 27, 2004 1825 Table_1825_DLBCL_1026_52170.txt 182,575 Aug. 27, 2004 1826 Table_1826_DLBCL_1027_52171.txt 182,667 Aug. 27, 2004 1827 Table_1827_DLBCL_1031_52172.txt 182,494 Aug. 27, 2004 1828 Table_1828_DLBCL_1034_52231.txt 182,455 Aug. 27, 2004 1829 Table_1829_DLBCL_1037_52200.txt 182,455 Aug. 30, 2004 1830 Table_1830_DLBCL_1038_52232.txt 182,577 Aug. 30, 2004 1831 Table_1831_DLBCL_1039_52233.txt 182,501 Aug. 30, 2004 1832 Table_1832_DLBCL_1041_52201.txt 182,599 Aug. 30, 2004 1833 Table_1833_DLBCL_1043_52202.txt 182,598 Aug. 30, 2004 1834 Table_1834_DLBCL_1045_52203.txt 182,555 Aug. 30, 2004 1835 Table_1835_DLBCL_1049_52204.txt 182,569 Aug. 30, 2004 1836 Table_1836_DLBCL_1051_52173.txt 182,324 Aug. 30, 2004 1837 Table_1837_DLBCL_1054_52174.txt 182,590 Aug. 30, 2004 1838 Table_1838_DLBCL_1055_52175.txt 182,433 Aug. 30, 2004 1839 Table_1838_DLBCL_1057_52176.txt 182,524 Aug. 30, 2004 1840 Table_1840_DLBCL_1058_52177.txt 182,386 Aug. 30, 2004 1841 Table_1841_DLBCL_1059_52178.txt 182,651 Aug. 30, 2004 1842 Table_1842_DLBCL_1060_52179.txt 182,633 Aug. 30, 2004 1843 Table_1843_DLBCL_1061_52180.txt 182,530 Aug. 30, 2004 1844 Table_1844_DLBCL_1990_52108.txt 182,550 Aug. 30, 2004 1845 Table_1845_DLBCL_1991_52109.txt 182,503 Aug. 30, 2004 1846 Table_1846_DLBCL_1994_52110.txt 182,422 Aug. 30, 2004 1847 Table_1847_DLBCL_2001_52111.txt 182,439 Aug. 30, 2004 1848 Table_1848_DLBCL_2017_52112.txt 182,354 Aug. 31, 2004 1849 Table_1849_DLBCL_2018_52113.txt 182,479 Aug. 31, 2004 1850 Table_1850_DLBCL_2095_52258.txt 182,492 Aug. 31, 2004 1851 Table_1851_DLBCL_304_52190.txt 182,498 Aug. 31, 2004 1852 Table_1852_DLBCL_305_52191.txt 182,502 Aug. 31, 2004 1853 Table_1853_DLBCL_306_52192.txt 182,549 Aug. 31, 2004 1854 Table_1854_DLBCL_309_52973.txt 182,434 Aug. 31, 2004 1855 Table_1855_DLBCL_310_52194.txt 182,489 Aug. 30, 2004 1856 Table_1856_DLBCL_412_52009.txt 182,344 Aug. 30, 2004 1857 Table_1857_DLBCL_413_52974.txt 182,340 Aug. 30, 2004 1858 Table_1858_DLBCL_415_51696.txt 182,507 Aug. 30, 2004 1859 Table_1859_DLBCL_421_52975.txt 182,519 Aug. 30, 2004 1860 Table_1860_DLBCL_424_51989.txt 182,537 Aug. 30, 2004 1861 Table_1861_DLBCL_428_51985.txt 182,415 Aug. 30, 2004 1862 Table_1862_DLBCL_432_52011.txt 182,400 Aug. 30, 2004 1863 Table_1863_DLBCL_433_52010.txt 182,501 Aug. 30, 2004 1864 Table_1864_DLBCL_434_51693.txt 182,610 Aug. 30, 2004 1865 Table_1865_DLBCL_438_51998.txt 182,437 Aug. 30, 2004 1866 Table_1866_DLBCL_439_52252.txt 182,417 Aug. 30, 2004 1867 Table_1867_DLBCL_446_52004.txt 182,554 Aug. 31, 2004 1868 Table_1868_DLBCL_449_51995.txt 182,556 Aug. 31, 2004 1869 Table_1869_DLBCL_452_52114.txt 182,501 Aug. 31, 2004 1870 Table_1870_DLBCL_458_51990.txt 182,494 Aug. 31, 2004 1871 Table_1871_DLBCL_459_51695.txt 182,583 Aug. 31, 2004 1872 Table_1872_DLBCL_460_51986.txt 182,618 Aug. 31, 2004 1873 Table_1873_DLBCL_462_51999.txt 182,431 Aug. 31, 2004 1874 Table_1874_DLBCL_470_52188.txt 182,400 Aug. 31, 2004 1875 Table_1875_DLBCL_477_52724.txt 182,482 Aug. 31, 2004 1876 Table_1876_DLBCL_479_52017.txt 182,484 Aug. 31, 2004 1877 Table_1877_DLBCL_481_51992.txt 182,632 Aug. 31, 2004 1878 Table_1878_DLBCL_482_52013.txt 182,394 Aug. 31, 2004 1879 Table_1879_DLBCL_491_51982.txt 182,481 Aug. 31, 2004 1880 Table_1880_DLBCL_492_51694.txt 182,573 Aug. 31, 2004 1881 Table_1881_DLBCL_517_52018.txt 182,479 Aug. 31, 2004 1882 Table_1882_DLBCL_523_52056.txt 182,366 Aug. 31, 2004 1883 Table_1883_DLBCL_524_51993.txt 182,690 Aug. 31, 2004 1884 Table_1884_DLBCL_528_52001.txt 182,438 Aug. 31, 2004 1885 Table_1885_DLBCL_529_52002.txt 182,388 Aug. 31, 2004 1886 Table_1886_DLBCL_533_52099.txt 182,603 Aug. 31, 2004 1887 Table_1887_DLBCL_537_52003.txt 182,396 Aug. 31, 2004 1888 Table_1888_DLBCL_538_51698.txt 182,531 Aug. 31, 2004 1889 Table_1889_DLBCL_541_52100.txt 182,500 Aug. 31, 2004 1890 Table_1890_DLBCL_543_52101.txt 182,754 Aug. 31, 2004 1891 Table_1891_DLBCL_544_52115.txt 182,571 Aug. 31, 2004 1892 Table_1892_DLBCL_545_52117.txt 182,644 Aug. 31, 2004 1893 Table_1893_DLBCL_547_52089.txt 182,539 Aug. 31, 2004 1894 Table_1894_DLBCL_549_52090.txt 182,569 Aug. 31, 2004 1895 Table_1895_DLBCL_550_51836.txt 182,321 Aug. 23, 2004 1896 Table_1896_DLBCL_553_52091.txt 182,456 Aug. 23, 2004 1897 Table_1897_DLBCL_565_52092.txt 182,388 Aug. 23, 2004 1898 Table_1898_DLBCL_572_52093.txt 182,395 Aug. 23, 2004 1899 Table_1899_DLBCL_577_52102.txt 182,336 Aug. 23, 2004 1900 Table_1900_DLBCL_615_52014.txt 182,286 Aug. 23, 2004 1901 Table_1901_DLBCL_616_52015.txt 182,089 Aug. 23, 2004 1902 Table_1902_DLBCL_617_52189.txt 182,485 Aug. 23, 2004 1903 Table_1903_DLBCL_618_52012.txt 182,239 Aug. 23, 2004 1904 Table_1904_DLBCL_619_52005.txt 182,336 Aug. 23, 2004 1905 Table_1905_DLBCL_623_51984.txt 182,212 Aug. 23, 2004 1906 Table_1906_DLBCL_625_52253.txt 182,476 Aug. 23, 2004 1907 Table_1907_DLBCL_626_52007.txt 182,302 Aug. 23, 2004 1908 Table_1908_DLBCL_627_52006.txt 182,273 Aug. 2$, 2004 1909 Table_1909_DLBCL_633_52150.txt 182,449 Aug. 23, 2004 1910 Table_1910_DLBCL_638_52151.txt 182,493 Aug. 23, 2004 1911 Table_1911_DLBCL_642_52254.txt 182,293 Aug. 23, 2004 1912 Table_1912_DLBCL_644_52116.txt 182,430 Aug. 23, 2004 1913 Table_1913_DLBCL_645_52118.txt 182,332 Aug. 23, 2004 1914 Table_1914_DLBCL_646_52119.txt 182,252 Aug. 23, 2004 1915 Table_1915_DLBCL_651_52121.txt 182,380 Aug. 23, 2004 1916 Table_1916_DLBCL_652_52120.txt 182,395 Aug. 23, 2004 1917 Table_1917_DLBCL_654_52016.txt 182,268 Aug. 23, 2004 1918 Table_1918_DLBCL_660_52057.txt 182,197 Aug. 23, 2004 1919 Table_1919_DLBCL_661_52058.txt 182,346 Aug. 23, 2004 1920 Table_1920_DLBCL_663_52071.txt 182,356 Aug. 23, 2004 1921 Table_1921_DLBCL_664_52072.txt 182,385 Aug. 23, 2004 1922 Table_1922_DLBCL_668_52073.txt 182,322 Aug. 23, 2004 1973 Table_1973_DLBCL_795_52156.txt 182,448 Aug. 23, 2004 1974 Table_1974_DLBCL_797_52103.txt 182,220 Aug. 23, 2004 1975 Table_1975_DLBCL_800_52337.txt 182,289 Aug. 23, 2004 1976 Table_1976_DLBCL_803_52163.txt 182,306 Aug. 23, 2004 1977 Table_1977_DLBCL_807_52104.txt 182,252 Aug. 23, 2004 1978 Table_1978_DLBCL_808_52126.txt 182,358 Aug. 23, 2004 1979 Table_1979_DLBCL_809_52105.txt 182,334 Aug. 23, 2004 1980 Table_1980_DLBCL_810_52106.txt 182,272 Aug. 23, 2004 1981 Table_1981_DLBCL_816_52985.txt 182,117 Aug. 23, 2004 1982 Table_1982_DLBCL_817_52986.txt 182,236 Aug. 23, 2004 1983 Table_1983_DLBCL_818_52184.txt 182,423 Aug. 23, 2004 1984 Table_1984_DLBCL_819_52987.txt 182,345 Aug. 23, 2004 1985 Table_1985_DLBCL_820_52988.txt 182,366 Aug. 23, 2004 1986 Table_1986_DLBCL_821_52339.txt 182,490 Aug. 23, 2004 1987 Table_1987_DLBCL_823_52989.txt 182,102 Aug. 23, 2004 1988 Table_1988_DLBCL_831_51996.txt 182,390 Aug. 23, 2004 1989 Table_1989_DLBCL_832_51997.txt 182,459 Aug. 23, 2004 1990 Table_1990_DLBCL_834_52186.txt 182,214 Aug. 23, 2004 1991 Table_1991_DLBCL_835_51988.txt 182,362 Aug. 23, 2004 1992 Table_1992_DLBCL_836_52000.txt 182,322 Aug. 23, 2004 1993 Table_1993_DLBCL_838_51987.txt 182,402 Aug. 23, 2004 1994 Table_1994_DLBCL_839_51991.txt 182,377 Aug. 23, 2004 1995 Table_1995_DLBCL_840_52990.txt 182,527 Aug. 23, 2004 1996 Table_1996_DLBCL_841_52087.txt 182,353 Aug. 23, 2004 1997 Table_1997_DLBCL_847_52187.txt 182,422 Aug. 23, 2004 1998 Table_1998_DLBCL_851_52991.txt 182,280 Aug. 23, 2004 1999 Table_1999_DLBCL_854_52088.txt 182,372 Aug. 23, 2004 2000 Table_2000_DLBCL_855_51837.txt 182,472 Aug. 23, 2004 2001 Table_2001_DLBCL_856_52992.txt 182,213 Aug. 23, 2004 2002 Table_2002_DLBCL_858_52993.txt 182,472 Aug. 23, 2004 2003 Table_2003_DLBCL_860_52195.txt 182,229 Aug. 23, 2004 2004 Table_2004_DLBCL_871_52196.txt 182,241 Aug. 23, 2004 2005 Table_2005_DLBCL_872_51700.txt 182,279 Aug. 23, 2004 2006 Table_2005_DLBCL_874_52107.txt 182,412 Aug. 23, 2004 2007 Table_2007_ULBOL_875_52246.txt 182,286 Aug. 23, 2004 2008 Table_2008_DLBCL_912_52197.txt 182,328 Aug. 23, 2004 2009 Table_2009_DLBCL_995_52198.txt 182,356 Aug. 23, 2004 2010 Table_2010_DLBCL_996_52199.txt 182,251 Aug. 23, 2004 2011 Table_2011_DLBCL_998_52157.txt 182,354 Aug. 23, 2004 2012 Table_2012_FH_2043_52774.txt 182,536 Aug. 23, 2004 2013 Table_2013_FH_2045_52775.txt 182,609 Aug. 23, 2004 2014 Table_2014_FH_2047_52776.txt 182,602 Aug. 23, 2004 2015 Table_2015_FH_2120_52777.txt 182,548 Aug. 23, 2004 2016 Table_2016_FH_2123_52778.txt 182,516 Aug. 23, 2004 2017 Table_2017_FH_2124_52779.txt 182,535 Aug. 23, 2004 2018 Table_2018_FH_2138_52780.txt 182,370 Aug. 23, 2004 2019 Table_2019_FH_2139_52781.txt 182,510 Aug. 23, 2004 2020 Table_2020_FH_2140_52782.txt 182,561 Aug. 23, 2004 2021 Table_2021_FH_2141_52783.txt 182,575 Aug. 23, 2004 2022 Table_2022_FH_2142_52784.txt 182,552 Aug. 23, 2004 2023 Table_2023_FH_2143_52785.txt 182,502 Aug. 23, 2004 2024 Table_2024_FH_2159_52786.txt 182,442 Aug. 23, 2004 2025 Table_2025_FH_2160_52787.txt 182,430 Aug. 23, 2004 2026 Table_2026_FH_2161_52788.txt 182,442 Aug. 23, 2004 2027 Table_2027_FH_2162_52789.txt 182,507 Aug. 23, 2004 2028 Table_2028_FH_2164_52790.txt 182,524 Aug. 23, 2004 2029 Table_2029_FH_2167_52791.txt 182,593 Aug. 23, 2004 2036 Table_2030_FL_1073_52458.txt 182,551 Aug. 23, 2004 2031 Table_2031_FL_1074_52459.txt 182,424 Aug. 23, 2004 2032 Table_2032_FL_1075_52460.txt 182,490 Aug. 23, 2004 2033 Table_2033_FL_1076_52461.txt 182,391 Aug. 23, 2004 2034 Table_2034_FL_1077_52463.txt 182,504 Aug. 23, 2004 2035 Table_2035_FL_1078_52464.txt 182,477 Aug. 23, 2004 2036 Table_2036_FL_1080_52465.txt 182,534 Aug. 23, 2004 2037 Table_2037_FL_1081_52660.txt 182,448 Aug. 23, 2004 2038 Table_2038_FL_1083_52466.txt 182,566 Aug. 23, 2004 2039 Table_2039_FL_1085_52467.txt 182,520 Aug. 23, 2004 2040 Table_2040_FL_1086_52468.txt 182,529 Aug. 23, 2004 2041 Table_2041_FL_1087_52469.txt 182,476 Aug. 23, 2004 2042 Table_2042_FL_1088_52470.txt 182,466 Aug. 23, 2004 2043 Table_2043_FL_1089_52471.txt 182,361 Aug. 23, 2004 2044 Table_2044_FL_1090_52472.txt 182,477 Aug. 23, 2004 2045 Table_2045_FL_1097_52473.txt 182,532 Aug. 23, 2004 2046 Table_2046_FL_1098_52474.txt 182,589 Aug. 23, 2004 2047 Table_2047_FL_1099_52475.txt 182,513 Aug. 23, 2004 2048 Table_2048_FL_1102_52648.txt 182,505 Aug. 23, 2004 2049 Table_2049_FL_1104_52649.txt 182,423 Aug. 23, 2004 2050 Table_2050_FL_1106_52476.txt 182,475 Aug. 23, 2004 2051 Table_2051_FL_1107_52650.txt 182,528 Aug. 23, 2004 2052 Table_2052_FL_1183_52651.txt 182,441 Aug. 23, 2004 2053 Table_2053_FL_1184_52652.txt 182,560 Aug. 23, 2004 2054 Table_2054_FL_1185_52653.txt 182,459 Aug. 23, 2004 2055 Table_2055_FL_1186_52654.txt 182,532 Aug. 23, 2004 2056 Table_2056_FL_1416_52655.txt 182,489 Aug. 23, 2004 2057 Table_2057_FL_1417_52656.txt 182,381 Aug. 23, 2004 2058 Table_2058_FL_1418_52657.txt 182,418 Aug. 23, 2004 2059 Table_2059_FL_1419_52658.txt 182,527 Aug. 23, 2004 2060 Table_2060_FL_1422_52659.txt 182,462 Aug. 23, 2004 2061 Table_2061_FL_1425_51703.txt 182,392 Aug. 23, 2004 2062 Table_2062_FL_1426_51702.txt 182,416 Aug. 23, 2004 2063 Table_2063_FL_1427_52997.txt 182,307 Aug. 23, 2004 2064 Table_2064_FL_1428_51705.txt 182,460 Aug. 23, 2004 2065 Table_2065_FL_1429_52661.txt 182,411 Aug. 23, 2004 2066 Table_2066_FL_1432_52999.txt 182,463 Aug. 23, 2004 2067 Table_2067_FL_1434_52662.txt 182,762 Aug. 23, 2004 2068 Table_2068_FL_1436_52663.txt 182,426 Aug. 23, 2004 2069 Table_2069_FL_1440_52664.txt 182,383 Aug. 23, 2004 2070 Table_2070_FL_1445_52665.txt 182,413 Aug. 23, 2004 2071 Table_2071_FL_1450_52666.txt 182,480 Aug. 23, 2004 2072 Table_2072_FL_1472_52667.txt 182,367 Aug. 23, 2004 2073 Table_2073_FL_1473_53000.txt 182,558 Aug. 23, 2004 2074 Table_2074_FL_1474_53001.txt 182,648 Aug. 23, 2004 2075 Table_2075_FL_1476_52668.txt 182,438 Aug. 23, 2004 2076 Table_2076_FL_1477_52669.txt 182,425 Aug. 23, 2004 2077 Table_2077_FL_1478_52670.txt 182,377 Aug. 23, 2004 2078 Table_2078_FL_1479_52671.txt 182,411 Aug. 23, 2004 2079 Table_2079_FL_1480_52672.txt 182,428 Aug. 23, 2004 2080 Table_2080_FL_1579_52673.txt 182,439 Aug. 23, 2004 2081 Tabl8_2081_FL_1580_52674.txt 182,552 Aug. 23, 2004 2082 Table_2082_FL_1581_52675.txt 182,497 Aug. 23, 2004 2083 Table_2083_FL_1552_52676.txt 182,370 Aug. 23, 2004 2084 Table_2084_FL_1583_52677.txt 182,535 Aug. 23, 2004 2085 Table_2085_FL_1584_52678.txt 182,524 Aug. 23, 2004 2086 Table_2086_FL_1585_52726.txt 182,443 Aug. 23, 2004 2087 Table_2087_FL_1586_52679.txt 182,465 Aug. 23, 2004 2088 Table_2088_FL_1588_52680.txt 182,490 Aug. 23, 2004 2089 Table_2089_FL_1589_52681.txt 182,477 Aug. 23, 2004 2090 Table_2090_FL_1591_52682.txt 182,513 Aug. 23, 2004 2091 Table_2091_FL_1594_52683.txt 182,498 Aug. 23, 2004 2092 Table_2092_FL_1595_52684.txt 182,547 Aug. 23, 2004 2093 Table_2093_FL_1598_52685.txt 182,415 Aug. 23, 2004 2094 Table_2004_FL_1599_52686.txt 182,531 Aug. 23, 2004 2095 Table_2095_FL_1603_52687.txt 182,544 Aug. 23, 2004 2090 Table_2096_FL_1604_52688.txt 182,394 Aug. 23, 2004 2097 Table_2097_FL_1606_52689.txt 182,440 Aug. 23, 2004 2098 Table_2098_FL_1607_52690.txt 182,527 Aug. 23, 2004 2099 Table_2099_FL_1608_52691.txt 182,440 Aug. 23, 2004 2100 Table_2100_FL_1610_52692.txt 182,452 Aug. 23, 2004 2101 Table_2101_FL_1611_52693.txt 182,488 Aug. 23, 2004 2102 Table_2102_FL_1616_52694.txt 182,438 Aug. 23, 2004 2103 Table_2103_FL_1617_52695.txt 182,496 Aug. 23, 2004 2104 Table_2104_FL_1619_52696.txt 182,544 Aug. 23, 2004 2105 Table_2105_FL_1622_52388.txt 182,452 Aug. 23, 2004 2106 Table_2106_FL_1623_52389.txt 182,521 Aug. 23, 2004 2107 Table_2107_FL_1624_52390.txt 182,474 Aug. 23, 2004 2108 Table_2108_FL_1625_52391.txt 182,364 Aug. 23, 2004 2109 Table_2109_FL_1626_52392.txt 182,350 Aug. 23, 2004 2110 Table_2110_FL_1627_52393.txt 182,558 Aug. 23, 2004 2111 Table_2111_FL_1628_52394.txt 182,561 Aug. 23, 2004 2112 Table_2112_FL_1637_52395.txt 182,562 Aug. 23, 2004 2113 Table_2113_FL_1638_53003.txt 182,388 Aug. 23, 2004 2114 Table_2114_FL_1639_52396.txt 182,466 Aug. 23, 2004 2115 Table_2115_FL_1643_52397.txt 182,502 Aug. 23, 2004 2116 Table_2116_FL_1644_52398.txt 182,453 Aug. 23, 2004 2117 Table_2117_FL_1645_52399.txt 182,430 Aug. 23, 2004 2118 Table_2118_FL_1646_52400.txt 182,457 Aug. 23, 2004 2119 Table_2119_FL_1647_52401.txt 182,386 Aug. 23, 2004 2120 Table_2120_FL_1648_52402.txt 182,358 Aug. 23, 2004 2121 Table_2121_FL_1652_52403.txt 182,506 Aug. 23, 2004 2122 Table_2122_FL_1654_53004.txt 182,392 Aug. 23, 2004 2123 Table_2123_FL_1655_52404.txt 182,479 Aug. 23, 2004 2124 Table_2124_FL_1656_52405.txt 182,364 Aug. 23, 2004 2125 Table_2125_FL_1657_52406.txt 182,439 Aug. 23, 2004 2126 Table_2126_FL_1660_52407.txt 182,458 Aug. 23, 2004 2127 Table_2127_FL_1661_52408.txt 182,602 Aug. 23, 2004 2128 Table_2128_FL_1662_52409.txt 182,469 Aug. 23, 2004 2129 Table_2129_FL_1664_52410.txt 182,507 Aug. 23, 2004 2130 Table_2130_FL_1669_52411.txt 182,451 Aug. 23, 2004 2131 Table_2131_FL_1670_52412.txt 182,471 Aug. 23, 2004 2132 Table_2132_FL_1675_52413.txt 182,442 Aug. 23, 2004 2133 Table_2133_FL_1681_52421.txt 182,476 Aug. 23, 2004 2134 Table_2134_FL_1683_52422.txt 182,531 Aug. 23, 2004 2135 Table_2135_FL_1684_52423.txt 182,455 Aug. 23, 2004 2136 Table_2136_FL_1716_52414.txt 182,416 Aug. 23, 2004 2137 Table_2137_FL_1717_52415.txt 182,526 Aug. 23, 2004 2138 Table_2138_FL_1718_53006.txt 182,411 Aug. 23, 2004 2139 Table_2139_FL_1719_52416.txt 182,449 Aug. 23, 2004 2140 Table_2140_FL_1720_53025.txt 182,424 Aug. 23, 2004 2141 Table_2141_FL_1729_52417.txt 182,495 Aug. 23, 2004 2142 Table_2142_FL_1732_52418.txt 182,340 Aug. 23, 2004 2143 Table_2143_FL_1735_52419.txt 182,212 Aug. 23, 2004 2144 Table_2144_FL_1761_52424.txt 182,512 Aug. 23, 2004 2145 Table_2145_FL_1764_52425.txt 182,533 Aug. 23, 2004 2146 Table_2146_FL_1768_52426.txt 182,520 Aug. 23, 2004 2147 Table_2147_FL_1771_52427.txt 182,487 Aug. 23, 2004 2148 Table_2148_FL_1772_52428.txt 182,547 Aug. 23, 2004 2149 Table_2149_FL_1788_52429.txt 182,481 Aug. 23, 2004 2150 Table_2150_FL_1790_53008.txt 182,535 Aug. 23, 2004 2151 Table_2151_FL_1792_52431.txt 182,458 Aug. 23, 2004 2152 Table_2152_FL_1795_52432.txt 182,548 Aug. 23, 2004 2153 Table_2153_FL_1797_52433.txt 182,519 Aug. 23, 2004 2154 Table_2154_FL_1799_52434.txt 182,430 Aug. 23, 2004 2155 Table_2155_FL_1810_52435.txt 182,362 Aug. 23, 2004 2456 Table_2156_FL_1811_52436.txt 182,501 Aug. 23, 2004 2157 Table_2157_FL_1825_52437.txt 182,460 Aug. 23, 2004 2158 Table_2158_FL_1827_52438.txt 182,528 Aug. 23, 2004 2159 Table_2159_FL_1828_52439.txt 182,506 Aug. 23, 2004 2160 Table_2160_FL_1829_52440.txt 182,533 Aug. 23, 2004 2161 Table_2161_FL_1830_52441.txt 182,448 Aug. 23, 2004 2162 Table_2162_FL_1833_52442.txt 182,523 Aug. 23, 2004 2163 Table_2163_FL_1834_52443.txt 182,477 Aug. 23, 2004 2164 Table_2164_FL_1835_52444.txt 182,469 Aug. 23, 2004 2165 Table_2165_FL_1836_52445.txt 182,504 Aug. 23, 2004 2166 Table_2166_FL_1837_52446.txt 182,453 Aug. 23, 2004 2167 Table_2167_FL_1838_52447.txt 182,465 Aug. 23, 2004 2168 Table_2168_FL_1839_52448.txt 182,343 Aug. 23, 2004 2169 Table_2169_FL_1841_52449.txt 182,524 Aug. 23, 2004 2170 Table_2170_FL_1842_52450.txt 182,510 Aug. 23, 2004 2171 Table_2171_FL_1844_52451.txt 182,456 Aug. 23, 2004 2172 Table_2172_FL_1845_52452.txt 182,582 Aug. 23, 2004 2173 Table_2173_FL_1846_52477.txt 182,514 Aug. 23, 2004 2174 Table_2174_FL_1848_52478.txt 182,510 Aug. 23, 2004 2175 Table_2175_FL_1850_52479.txt 182,504 Aug. 23, 2004 2176 Table_2176_FL_1851_52480.txt 182,536 Aug. 23, 2004 2177 Table_2177_FL_1853_52481.txt 182,501 Aug. 23, 2004 2178 Table_2178_FL_1854_52482.txt 182,554 Aug. 23, 2004 2179 Table_2179_FL_1855_52483.txt 182,515 Aug. 23, 2004 2180 Table_2180_FL_1857_52484.txt 182,644 Aug. 23, 2004 2181 Table_2181_FL_1861_52453.txt 182,539 Aug. 23, 2004 2182 Table_2182_FL_1862_52454.txt 182,489 Aug. 23, 2004 2183 Table_2183_FL_1863_52455.txt 182,508 Aug. 23, 2004 2184 Table_2184_FL_1864_52456.txt 182,617 Aug. 23, 2004 2185 Table_2185_FL_1866_52457.txt 182,516 Aug. 23, 2004 2186 Table_2186_FL_1870_52697.txt 182,562 Aug. 23, 2004 2187 Table_2187_FL_1873_52698.txt 182,436 Aug. 23, 2004 2188 Table_2188_FL_1874_52699.txt 182,463 Aug. 23, 2004 2189 Table_2189_FL_1876_52700.txt 182,466 Aug. 23, 2004 2190 Table_2190_FL_1879_52701.txt 182,465 Aug. 23, 2004 2191 Table_2191_FL_1880_52702.txt 182,458 Aug. 23, 2004 2192 Table_2192_FL_1882_52704.txt 182,512 Aug. 23, 2004 2193 Table_2193_FL_1884_52705.txt 182,489 Aug. 23, 2004 2194 Table_2194_FL_1885_52706.txt 182,507 Aug. 23, 2004 2195 Table_2195_FL_1887_52707.txt 182,481 Aug. 23, 2004 2196 Table_2196_FL_1888_52708.txt 182,550 Aug. 23, 2004 2197 Table_2197_FL_1890_52709.txt 182,428 Aug. 23, 2004 2198 Table_2198_FL_1894_52710.txt 182,442 Aug. 23, 2004 2199 Table_2199_FL_1896_52711.txt 182,482 Aug. 23, 2004 2200 Table_2200_FL_1897_52756.txt 182,367 Aug. 23, 2004 2201 Table_2201_FL_1898_52757.txt 182,315 Aug. 23, 2004 2202 Table_2202_FL_1900_52712.txt 182,352 Aug. 23, 2004 2203 Table_2203_FL_1903_52713.txt 182,354 Aug. 23, 2004 2204 Table_2204_FL_1904_52714.txt 182,421 Aug. 23, 2004 2205 Table_2205_FL_1905_52715.txt 182,450 Aug. 23, 2004 2206 Table_2206_FL_1906_53010.txt 182,295 Aug. 23, 2004 2207 Table_2207_FL_1907_52717.txt 182,268 Aug. 23, 2004 2208 Table_2208_FL_1910_52718.txt 182,425 Aug. 23, 2004 2209 Table_2209_FL_1912_52719.txt 182,519 Aug. 23, 2004 2210 Table_2210_FL_1913_52720.txt 182,464 Aug. 23, 2004 2211 Table_2211_FL_1916_52721.txt 182,418 Aug. 23, 2004 2212 Table_2212_FL_1918_52722.txt 182,529 Aug. 23, 2004 2213 Table_2213_FL_1919_52723.txt 182,388 Aug. 23, 2004 2214 Table_2214_FL_735_52462.txt 182,452 Aug. 23, 2004 2215 Table_2215_FL_738_52642.txt 182,363 Aug. 23, 2004 2216 Table_2216_FL_739_52643.txt 182,462 Aug. 23, 2004 2217 Table_2217_FL_878_52644.txt 182,344 Aug. 23, 2004 2218 Table_2218_FL_879_52645.txt 182,472 Aug. 23, 2004 2219 Table_2219_FL_886_52646.txt 182,483 Aug. 23, 2004 2220 Table_2220_FL_888_52647.txt 182,373 Aug. 23, 2004 2221 Table_2221_GMALT_2021_52795.txt 182,498 Aug. 23, 2004 2222 Table_2222_GMALT_2041_52796.txt 182,517 Aug. 23, 2004 2223 Table_2223_GMALT_2065_52797.txt 182,535 Aug. 23, 2004 2224 Table_2224_GMALT_2067_52798.txt 182,459 Aug. 23, 2004 2225 Table_2225_GMALT_2070_52799.txt 182,458 Aug. 23, 2004 2226 Table_2226_GMALT_2110_52800.txt 182,534 Aug. 23, 2004 2227 Table_2227_GMALT_2111_52801.txt 182,432 Aug. 23, 2004 2228 Table_2228_GMALT_2112_52802.txt 182,478 Aug. 23, 2004 2229 Table_2229_GMALT_2270_52803.txt 182,595 Aug. 23, 2004 2230 Table_2230_LBL_2185_53023.txt 182,457 Aug. 23, 2004 2231 Table_2231_LBL_2186_52823.txt 182,329 Aug. 23, 2004 2232 Table_2232_LBL_2249_52824.txt 182,381 Aug. 23, 2004 2233 Table_2233_LPC_2224_52842.txt 182,646 Aug. 23, 2004 2234 Table_2234_LPC_2232_52843.txt 182,608 Aug. 23, 2004 2235 Table_2235_LPC_2261_52821.txt 182,504 Aug. 23, 2004 2236 Table_2236_LPC_2262_53542.txt 182,616 Aug. 23, 2004 2237 Table_2237_LPC_2263_52844.txt 182,430 Aug. 23, 2004 2238 Table_2238_LPC_2265_52845.txt 182,451 Aug. 23, 2004 2239 Table_2239_LPC_2410_53371.txt 182,412 Aug. 23, 2004 2240 Table_2240_LPC_2411_53372.txt 182,521 Aug. 23, 2004 2241 Table_2241_LPC_2412_53373.txt 182,573 Aug. 23, 2004 2242 Table_2242_LPC_2413_53374.txt 182,501 Aug. 23, 2004 2243 Table_2243_LPC_2414_53375.txt 182,489 Aug. 23, 2004 2244 Table_2244_MALT_2178_52827.txt 182,515 Aug. 23, 2004 2245 Table_2245_MALT_2181_52828.txt 182,440 Aug. 23, 2004 2246 Table_2246_MALT_2182_52829.txt 182,507 Aug. 23, 2004 2247 Table_2247_MALT_2183_52830.txt 182,467 Aug. 23, 2004 2248 Table_2248_MALT_2184_52831.txt 182,543 Aug. 23, 2004 2249 Table_2249_MALT_2202_52833.txt 182,668 Aug. 23, 2004 2250 Table_2250_MALT_2211_52836.txt 182,623 Aug. 23, 2004 2251 Table_2251_MALT_2243_52832.txt 182,505 Aug. 23, 2004 2252 Table_2252_MCL_1012_51716.txt 182,396 Aug. 23, 2004 2253 Table_2253_MCL_1091_52328.txt 182,428 Aug. 23, 2004 2254 Table_2254_MCL_1114_52329.txt 182,400 Aug. 23, 2004 2255 Table_2255_MCL_1128_51717.txt 182,335 Aug. 23, 2004 2256 Table_2256_MCL_1150_51714.txt 182,428 Aug. 23, 2004 2257 Table_2257_MCL_1162_51715.txt 182,412 Aug. 23, 2004 2258 Table_2258_MCL_1166_52330.txt 182,469 Aug. 23, 2004 2259 Table_2259_MCL_1194_52331.txt 181,991 Aug. 23, 2004 2260 Table_2260_MCL_885_53011.txt 182,459 Aug. 23, 2004 2261 Table_2261_MCL_918_52316.txt 182,550 Aug. 23, 2004 2262 Table_2262_MCL_924_52318.txt 182,557 Aug. 23, 2004 2263 Table_2263_MCL_925_52317.txt 182,463 Aug. 23, 2004 2264 Table_2264_MCL_926_52319.txt 182,485 Aug. 23, 2004 2265 Table_2265_MCL_936_52320.txt 182,582 Aug. 23, 2004 2266 Table_2266_MCL_939_52321.txt 182,610 Aug. 23, 2004 2267 Table_2267_MCL_953_52322.txt 182,492 Aug. 23, 2004 2268 Table_2268_MCL_956_52323.txt 182,482 Aug. 23, 2004 2269 Table_2269_MCL_964_52324.txt 182,429 Aug. 23, 2004 2270 Table_2270_MCL_966_52325.txt 182,395 Aug. 23, 2004 2271 Table_2271_MCL_968_52326.txt 182,403 Aug. 23, 2004 2272 Table_2272_MCL_970_52327.txt 182,434 Aug. 23, 2004 2273 Table_2273_NMZ_2231_52834.txt 182,506 Aug. 23, 2004 2274 Table_2274_NMZ_2242_52835.txt 182,434 Aug. 23, 2004 2275 Table_2275_NMZ_2254_52846.txt 182,612 Aug. 23, 2004 2276 Table_2276_NMZ_2416_53376.txt 182,442 Aug. 23, 2004 2277 Table_2277_PMBL_1048_52288.txt 182,443 Aug. 23, 2004 2278 Table_2278_PMBL_1053_52289.txt 182,385 Aug. 23, 2004 2279 Table_2279_PMBL_1920_53016.txt 182,435 Aug. 23, 2004 2280 Table_2280_PMBL_1921_51713.txt 182,362 Aug. 23, 2004 2281 Table_2281_PMBL_1923_51712.txt 182,440 Aug. 23, 2004 2282 Table_2282_PMBL_1924_51711.txt 182,419 Aug. 23, 2004 2283 Table_2283_PMBL_1935_52290.txt 182,426 Aug. 23, 2004 2284 Table_2284_PMBL_1941_52291.txt 182,477 Aug. 23, 2004 2285 Table_2285_PMBL_1942_52292.txt 182,441 Aug. 23, 2004 2286 Table_2286_PMBL_1943_52293.txt 182,492 Aug. 23, 2004 2287 Table_2287_PMBL_1945_52825.txt 182,346 Aug. 23, 2004 2288 Table_2288_PMBL_1946_52826.txt 182,211 Aug. 23, 2004 2289 Table_2289_PMBL_1948_52294.txt 182,305 Aug. 23, 2004 2290 Table_2290_PMBL_1949_52297.txt 182,453 Aug. 23, 2004 2291 Table_2291_PMBL_1989_52298.txt 182,376 Aug. 23, 2004 2292 Table_2292_PMBL_1992_52299.txt 182,469 Aug. 23, 2004 2293 Table_2293_PMBL_1993_52300.txt 182,356 Aug. 23, 2004 2204 Table_2294_PMBL_2002_52301.txt 182,424 Aug. 23, 2004 2295 Table_2295_PMBL_2019_52302.txt 182,358 Aug. 23, 2004 2296 Table_2296_PMBL_2020_52303.txt 182,617 Aug. 30, 2004 2297 Table_2297_PMBL_2092_52304.txt 182,611 Aug. 30, 2004 2298 Table_2298_PMBL_484_52279.txt 182,564 Aug. 30, 2004 2299 Table_2299_PMBL_546_53018.txt 182,646 Aug. 28, 2004 2300 Table_2300_PMBL_570_52281.txt 182,653 Aug. 28, 2004 2301 Table_2301_PMBL_621_52282.txt 182,458 Aug. 28, 2004 2302 Table_2302_PMBL_691_52283.txt 182,463 Aug. 28, 2004 2303 Table_2303_PMBL_791_52284.txt 182,606 Aug. 28, 2004 2304 Table_2304_PMBL_824_52285.txt 182,716 Aug. 28, 2004 2305 Table_2305_PMBL_906_52286.txt 182,549 Aug. 28, 2004 2306 Table_2306_PMBL_994_52287.txt 182,707 Aug. 28, 2004 2307 Table_2307_PTCL_2311_52837.txt 182,602 Aug. 28, 2004 2308 Table_2308_PTCL_2312_52838.txt 182,690 Aug. 28, 2004 2309 Table_2309_PTCL_2315_52839.txt 182,718 Aug. 28, 2004 2310 Table_2310_PTCL_2318_52841.txt 182,568 Aug. 28, 2004 2311 Table_2311_PTCL_2319_52840.txt 182,541 Aug. 28, 2004 2312 Table_2312_PTCL_2320_53292.txt 182,652 Aug. 28, 2004 2313 Table_2313_PTLD_1817_52820.txt 182,507 Aug. 28, 2004 2314 Table_2314_PTLD_1824_52819.txt 182,673 Aug. 28, 2004 2315 Table_2315_PTLD_1981_52818.txt 182,480 Aug. 28, 2004 2316 Table_2316_PTLD_1986_52817.txt 182,509 Aug. 28, 2004 2317 Table_2317_SLL_1151_52374.txt 182,653 Aug. 28, 2004 2318 Table_2318_SLL_1153_52375.txt 182,426 Aug. 28, 2004 2319 Table_2319_SLL_1155_52376.txt 182,449 Aug. 28, 2004 2320 Table_2320_SLL_1156_52377.txt 182,557 Aug. 28, 2004 2321 Table_2321_SLL_1158_52378.txt 182,490 Aug. 28, 2004 2322 Table_2322_SLL_1213_53026.txt 182,692 Aug. 27, 2004 2323 Table_2323_SLL_1214_51723.txt 182,570 Aug. 27, 2004 2324 Table_2324_SLL_1216_53027.txt 182,454 Aug. 27, 2004 2325 Table_2325_SLL_1217_53028.txt 182,541 Aug. 27, 2004 2326 Table_2326_SLL_1218_52379.txt 182,555 Aug. 27, 2004 2327 Table_2327_SLL_1220_52380.txt 182,512 Aug. 27, 2004 2328 Table_2328_SLL_1229_52381.txt 182,500 Aug. 27, 2004 2329 Table_2329_SLL_1231_52382.txt 182,436 Aug. 27, 2004 2330 Table_2330_SLL_1235_52383.txt 182,465 Aug. 27, 2004 2331 Table_2331_SLL_1236_52384.txt 182,698 Aug. 27, 2004 2332 Table_2332_SLL_1238_52385.txt 182,402 Aug. 27, 2004 2333 Table_2333_SLL_1240_52386.txt 182,578 Aug. 27, 2004 2334 Table_2334_SLL_1620_52387.txt 182,607 Aug. 27, 2004 2335 Table_2335_SPL_2061_52804.txt 182,693 Aug. 27, 2004 2336 Table_2336_SPL_2074_52805.txt 182,658 Aug. 27, 2004 2337 Table_2337_SPL_2075_52806.txt 182,548 Aug. 27, 2004 2338 Table_2338_SPL_2077_52807.txt 182,653 Aug. 27, 2004 2339 Table_2339_SPL_2079_52808.txt 182,652 Aug. 27, 2004 2340 Table_2340_SPL_2104_52809.txt 182,643 Aug. 27, 2004 2341 Table_2341_SPL_2105_52810.txt 182,682 Aug. 27, 2004 2342 Table_2342_SPL_2106_52811.txt 182,585 Aug. 27, 2004 2343 Table_2343_SPL_2256_52812.txt 182,721 Aug. 27, 2004 2344 Table_2344_SPL_2257_52813.txt 182,754 Aug. 27, 2004 2345 Table_2345_SPL_2258_52814.txt 182,578 Aug. 27, 2004 2346 Table_2346_SPL_2259_52815.txt 182,644 Aug. 27, 2004 2347 Table_2347_SPL_2260_52816.txt 182,662 Aug. 27, 2004 2348 Table_2348_trDLBCL_1332_52305.txt 182,606 Aug. 27, 2004 2349 Table_2349_trDLBCL_1336_53020.txt 182,204 Aug. 27, 2004 2350 Table_2350_trDLBCL_1338_52307.txt 182,460 Aug. 27, 2004 2351 Table_2351_trDLBCL_1353_52308.txt 182,637 Aug. 27, 2004 2352 Table_2352_trDLBCL_1357_52309.txt 182,640 Aug. 27, 2004 2353 Table_2353_trDLBCL_1508_52310.txt 182,613 Aug. 27, 2004 2354 Table_2354_trDLBCL_1520_52311.txt 182,675 Aug. 27, 2004 2355 Table_2355_trDLBCL_1535_52312.txt 182,651 Aug. 27, 2004 2356 Table_2356_trDLBCL_1782_52313.txt 182,621 Aug. 27, 2004 2357 Table_2357_trDLBCL_1784_52314.txt 182,752 Aug. 27, 2004 2358 Table_2358_trDLBCL_1786_52725.txt 182,570 Aug. 24, 2004 — Table of Contents.txt 146,716 Sep. 2, 2004 Disc 22 of 22 File Name File size (bytes) Date Created GeneData.txt 9,800,834 Aug. 31, 2004 GeneID.txt 319,333 Aug. 31, 2004 PredictionResults.txt 50,889 Sep. 2, 2004 SampleID.txt 10,117 Sep. 2, 2004 SubtypePredictor.txt 16,614 Aug. 31, 2004 Table of contents.txt 255 Sep. 2, 2004

As stated above, the foregoing is merely intended to illustrate various embodiments of the present invention. The specific modifications discussed above are not to be construed as limitations on the scope of the invention. It will be apparent to one skilled in the art that various equivalents, changes, and modifications may be made without departing from the scope of the invention, and it is understood that such equivalent embodiments are to be included herein. All references cited herein are incorporated by reference as if fully set forth herein.

Abbreviations used herein: ABC, activated B-cell-like diffuse large B cell lymphoma; BL, Burkitt lymphoma; CHOP, cyclophosphamide, doxorubicine, vincristine, and prednisone; CI, confidence interval; CNS, central nervous system; DLBCL, diffuse large B-cell lymphoma; ECOG, Eastern Cooperative Oncology Group; EST, expressed sequence tag; FACS, fluorescence-activated cell sorting; FH, follicular hyperplasia; FL, follicular lymphoma; GCB, germinal center B-cell-like diffuse large B cell lymphoma; IPI, International Prognostic Index; LPC, lymphoplasmacytic lymphoma; LPS, linear predictor score; MALT, mucosa-associated lymphoid tissue lymphomas; MCL, mantle cell lymphoma; MHC, major histocompatibility complex; NA, not available; NK, natural killer; NMZ, nodal marginal zone lymphoma; PCR, polymerase chain reaction; PMBL, primary mediastinal B-cell lymphoma; PTLD, post-transplant lymphoproliferative disorder; REAL, Revised European-American Lymphoma; RPA, RNase protection assay; RR, relative risk of death; RT-PCR, reverse transcriptase polymerase chain reaction; SAGE, serial analysis of gene expression; SLL, small lymphocytic lymphoma; WHO, World Health Organization.

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1. A method for determining the lymphoma type of a sample X comprising the steps of: a) isolating gene expression product from lymphoma sample X; b) creating one or more lymphoma type pairs, wherein each lymphoma type pair represents a combination of a first lymphoma type and a second lymphoma type; c) for each lymphoma type pair, obtaining gene expression data for a set of genes G in said first lymphoma type and said second lymphoma type; d) calculating a series of scale factors, wherein each scale factor represents a difference in gene expression between said first lymphoma type and said second lymphoma type for one of the genes belonging to said set of genes G; e) identifying a subset of genes g that are differentially expressed between said first lymphoma type and said second lymphoma type; f) generating a series of linear predictor scores for a set of known samples belonging to said first lymphoma type and a set of known samples belonging to said second lymphoma type based on the expression of said subset of genes g identified in step (e); g) contacting the gene expression product from sample X to a plurality of probes to thereby obtain gene expression data for said subset of genes g for sample X; h) generating a linear predictor score for sample X based on the expression of said subset of genes g; i) calculating a probability q that sample X belongs to said first lymphoma type by: $q = \frac{\phi\left( {{{{LPS}(X)};{\hat{\mu}}_{1}},{\hat{\sigma}}_{1}} \right)}{{\phi\left( {{{{LPS}(X)};{\hat{\mu}}_{1}},{\hat{\sigma}}_{1}} \right)} + {\phi\left( {{{{LPS}(X)};{\hat{\mu}}_{2}},{\hat{\sigma}}_{2}} \right)}}$ wherein LPS(X) is the linear predictor score for sample X, φ(x; μ, σ) is the normal density function with mean μ and standard deviation σ, {circumflex over (μ)}₁ and {circumflex over (σ)}₁ are the mean and variance of the linear predictor scores for said set of known samples belonging to said first lymphoma type, and {circumflex over (μ)}₂ and {circumflex over (σ)}₂ are the mean and variance of the linear predictor scores for said known samples belonging to said second lymphoma type; j) determining whether probability q is a high probability, middle probability, or low probability value, wherein the high probability and the middle probability have an upper cut-off point between 0.5 and 0.99, and the middle probability and the low probability have a lower cut-off point between 0.01 and 0.5; and k) providing probability q to a user, wherein a high probability q indicates that sample X belongs to said first lymphoma type, a low probability q indicates that sample X belongs to said second lymphoma type, and a middle probability q indicates that sample X belongs to neither lymphoma type.
 2. The method of claim 1, wherein said subset of genes g contains z genes from said set of genes G with the largest scale factors, wherein z is from 5 to
 100. 3. The method of claim 2, wherein z=100.
 4. The method of claim 2, wherein said series of linear predictor scores in step (f) comprises one or more linear predictor scores generated using from 1 to z of the genes from said subset of genes g.
 5. The method of claim 2, further comprising the additional step of selecting a number of genes from 1 to z that generates the largest difference in linear predictor score between said first lymphoma type and said second lymphoma type, wherein the gene expression data obtained for sample X in step (g) is obtained only for said selected number of genes.
 6. The method of claim 1, wherein step (c) further comprises placing each gene in said set of genes G into one of n gene-list categories, wherein placement in a gene-list category indicates correlation between expression of said gene and expression of a gene expression signature.
 7. The method of claim 6, wherein said subset of genes g excludes genes belonging to a proliferation gene expression signature and genes belonging to a lymph node gene expression signature.
 8. The method of claim 6, wherein n=3.
 9. The method of claim 8, wherein said gene-list categories are a lymph node gene expression signature, a proliferation gene expression signature, and a standard gene expression signature, wherein said standard gene expression signature includes those genes not included in said lymph node and proliferation gene expression signatures.
 10. The method of claim 9, wherein said series of linear predictor scores in step (f) comprises four linear predictor scores for each gene in said subset of genes g, wherein: a) the first linear predictor score is generated using genes from the lymph node, proliferation, and standard gene expression signatures; b) the second linear predictor score is generated using genes from the standard gene expression signature only; c) the third linear predictor score is generated using genes from the standard and proliferation gene expression signatures only; and d) the fourth linear predictor score is generated using genes from the standard and lymph node gene expression signatures only.
 11. The method of claim 1 wherein the upper cut-off point between said high probability q and said middle probability q and the lower cut-off point between said middle probability q and said low probability q are determined by the following steps: i) ranking samples of known lymphoma type according to their probability q, wherein the samples of known lymphoma type include samples of the first lymphoma type and samples of the second lymphoma type; ii) analyzing each potential upper and lower cut-off point between adjacent samples in the ranking by: 3.99*[(% of said first lymphoma type misidentified as said second lymphoma type)+(% of said second lymphoma type misidentified as said first lymphoma type)]+[(% of said first lymphoma type classified as belonging to neither lymphoma type)+(% of said second lymphoma type classified as belonging to neither lymphoma type)]; and iii) selecting the upper cut-off point and the lower cutoff point, wherein the upper and the lower cut-off points are those that minimize the equation of ii).
 12. The method of claim 1 wherein the linear predictor scores are calculated by: ${{{LPS}(S)} = {\sum\limits_{j \in G}{t_{j}S_{j}}}},$ wherein S_(j) is the expression of gene j in a sample S and t_(j) is the scale factor representing the difference in expression of gene j between said first lymphoma type and said second lymphoma type.
 13. The method of claim 1 wherein said scale factors are t-statistics.
 14. The method of claim 1, wherein steps (c) and/or (g) further comprise the use of a microarray.
 15. The method of claim 1, wherein said sample X is classified as said first lymphoma type if said probability q is greater than 90%.
 16. The method of claim 1, wherein said first lymphoma type and said second lymphoma type are independently selected from the group consisting of: follicular lymphoma (FL), Burkitt lymphoma (BL), mantle cell lymphoma (MCL), follicular hyperplasia (FH), small cell lymphocytic lymphoma (SLL), mucosa-associated lymphoid tissue lymphoma (MALT), splenic lymphoma, multiple myeloma, lymphoplasmacytic lymphoma, post-transplant lymphoproliferative disorder (PTLD), lymphoblastic lymphoma, nodal marginal zone lymphoma (NMZ), germinal center B cell-like diffuse large B cell lymphoma (GCB DLBCL), activated B cell-like diffuse large B cell lymphoma (ABC DLBCL) and primary mediastinal B cell lymphoma (PMBL).
 17. The method of claim 1, wherein said first lymphoma type is mantle cell lymphoma (MCL) and said second lymphoma type is activated B cell-like diffuse large B cell lymphoma (DLBCL), and wherein said subset of genes g includes one or more genes selected from the group consisting of genes corresponding to (listed by GENBANK accession number): AK021895.1, NM_(—)001001567.1, NM_(—)002673.3, NM_(—)003108.3, BC025340.1, NM_(—)021211.2, NM_(—)022037.1 NM_(—)025249.2, NM_(—)018387.2, BF196503.1, AF251293.1, NM_(—)003498.4, AL110204.1, NM_(—)178335.1, NM_(—)001003789.1, NM_(—)052909.3, XM_(—)001124203.1, NM_(—)003890.2, NM_(—)021992.2, and NM_(—)170672.1.
 18. The method of claim 1, wherein said first lymphoma type is mantle cell lymphoma (MCL) and said second lymphoma type is Burkitt lymphoma (BL), and wherein said subset of genes g includes one or more genes selected from the group consisting of genes corresponding to (listed by GENBANK accession number): NM_(—)004445.2, NM_(—)019064.3, BC025340.1, NM_(—)004944.2, NM_(—)052909.3, AW977010.1, NM_(—)003436.2, NM_(—)178335.1, NM_(—)003108.3, NM_(—)014638.2, NM_(—)012276.3, NM_(—)014698.1, AW135407.1, NM_(—)022552.3, NM_(—)001099269.1, AJ296290.1, NM_(—)024761.3, NM_(—)012408.3, NM_(—)004305.2, and NM_(—)001024847.2.
 19. The method of claim 1, wherein said first lymphoma type is mantle cell lymphoma (MCL) and said second lymphoma type is follicular hyperplasia (FH), and wherein said subset of genes g includes one or more genes selected from the group consisting of genes corresponding to (listed by GENBANK accession number): NM_(—)003108.3, NM_(—)030753.3, AI694444.1, NM_(—)017784.3, NM_(—)052909.3, NM_(—)016083.3, NM_(—)000698.2, NM_(—)020805.1, NM_(—)001013746.1, NM_(—)003621.1, NM_(—)013230.2, NM_(—)001098503.1, NM_(—)138738.2, NM_(—)172004.2, NM_(—)153026.1, NM_(—)013230.2, XM_(—)001128367.1, NM_(—)012417.2, NM_(—)014792.2, and AF161538.1.
 20. The method of claim 1, wherein said first lymphoma type is mantle cell lymphoma (MCL) and said second lymphoma type is follicular lymphoma (FL), and wherein said subset of genes g includes one or more genes selected from the group consisting of genes corresponding to (listed by GENBANK accession number): NM_(—)003108.3, NM_(—)022552.3, AK021895.1, NM_(—)002673.3, BC025340.1, NM_(—)178335.1, NM_(—)004772.1, NM_(—)015915.3, NM_(—)001853.3, NM_(—)014322.2, NM_(—)032488.2, NM_(—)153026.1, NM_(—)003105.3, NM_(—)005522.4, NM_(—)001706.2, NM_(—)015150.1, AI809213.1, NM_(—)001706.2, NM_(—)002927.3, and NM_(—)170672.1.
 21. The method of claim 1, wherein said first lymphoma type is mantle cell lymphoma (MCL) and said second lymphoma type is germinal center B cell-like diffuse large B cell lymphoma (GCB DLBCL), and wherein said subset of genes g includes one or more genes selected from the group consisting of genes corresponding to (listed by GENBANK accession number): NM_(—)178335.1, NM_(—)003108.3, NM_(—)002673.3, NM_(—)024761.3, AK021895.1, NM_(—)022552.3, NM_(—)014322.2, NM_(—)003498.4, NM_(—)005921.1, NM_(—)017699.2, NM_(—)001099269.1, NM_(—)001025616.2, NM_(—)052909.3, NM_(—)004445.2, AI479031.1, NM_(—)0220371.1, NM_(—)001400.3, NM_(—)153026.1, NM_(—)001003819.1, and NM_(—)001706.2.
 22. The method of claim 1, wherein said first lymphoma type is mantle cell lymphoma (MCL) and said second lymphoma type is mucosa-associated lymphoid tissue lymphoma (MALT), and wherein said subset of genes g includes one or more genes selected from the group consisting of genes corresponding to (listed by GENBANK accession number): NM_(—)003108.3, NM_(—)052909.3, NM_(—)030753.3, NM_(—)006805.3, NR_(—)003238.1, NM_(—)153026.1, NM_(—)004772.1, NM_(—)001024401.2, AK021895.1, NM_(—)021727.3, NM_(—)001400.3, NM_(—)153026.1, NM_(—)003436.2, NM_(—)022552.3, NM_(—)018387.2, NM_(—)006614.2, NM_(—)001098503.1, AF251293.1, NM_(—)006866.1, and NM_(—)021727.3.
 23. The method of claim 1, wherein said first lymphoma type is mantle cell lymphoma (MCL) and said second lymphoma type is primary mediastinal B cell lymphoma (PMBL), and wherein said subset of genes g includes one or more genes selected from the group consisting of genes corresponding to (listed by GENBANK accession number): NM_(—)003108.3, NM_(—)030753.3, AF251293.1, NM_(—)178335.1, NM_(—)006352.3, NM_(—)002738.5, AK021895.1, NM_(—)005449.3NM_(—)000024.4, NM_(—)138738.2, BC047541.1, AW516128.1, NM_(—)021211.2, NM_(—)004516.2, NM_(—)006850.2, NM_(—)001002909.1, NM_(—)153026.1, NM_(—)012290.3, NM_(—)015184.3, and NM_(—)006860.2.
 24. The method of claim 1, wherein said first lymphoma type is mantle cell lymphoma (MCL) and said second lymphoma type is post-transplant lymphoproliferative disorder (PTLD), and wherein said subset of genes g includes one or more genes selected from the group consisting of genes corresponding to (listed by GENBANK accession number): BG749488.1, NM_(—)013230.2, NM_(—)013230.2, NM_(—)001098670.1, NM_(—)017784.3, NM_(—)005449.3, NM_(—)003108.3, NM_(—)003436.2, NM_(—)005921.1, NM_(—)001025108.1, NM_(—)004834.3, NM_(—)001400.3, NM_(—)001007231.1, AL833385.1, NM_(—)006614.2, NM_(—)014767.1, NM_(—)001104925.1, NM_(—)032966.1, NM_(—)000917.2, and NM_(—)005504.4.
 25. The method of claim 1, wherein said first lymphoma type is mantle cell lymphoma (MCL) and said second lymphoma type is small cell lymphocytic lymphoma (SLL), and wherein said subset of genes g includes one or more genes selected from the group consisting of genes corresponding to (listed by GENBANK accession number): NM_(—)003108.3, NM_(—)052909.3, AK021895.1, NM_(—)022552.3, NM_(—)004772.1, NM_(—)021727.3, NM_(—)021727.3, NM_(—)015183.1, B1759100.1, NM_(—)015183.1, NM_(—)015180.4, NM_(—)015183.1, NM_(—)145693.1, NM_(—)024507.2, NM_(—)002356.5, NM_(—)006614.2, NM_(—)006317.3, NM_(—)001498.2, NM_(—)002372.2, and NM_(—)001039538.1.
 26. The method of claim 1, wherein said first lymphoma type is mantle cell lymphoma (MCL) and said second lymphoma type is splenic lymphoma, and wherein said subset of genes g includes one or more genes selected from the group consisting of genes corresponding to (listed by GENBANK accession number): NM_(—)052909.3, NM_(—)003436.2, BC047698.1, NM_(—)133368.1, NM_(—)006805.3, NM_(—)003030.3, NM_(—)001006684.1, NM_(—)001105539.1, NM_(—)002814.2, NM_(—)003108.3, NM_(—)002356.5, NM_(—)014456.3, NM_(—)177531.4, NM_(—)001008495.2, NM_(—)018387.2, NM_(—)000516.4, NM_(—)001001992.1, NM_(—)001024401.2, NM_(—)007014.3, and NM_(—)001030005.
 27. The method of claim 1, wherein said first lymphoma type is activated B cell-like diffuse large B cell lymphoma (ABC DLBCL) and said second lymphoma type is germinal center B cell-like diffuse large B cell lymphoma (GCB DLBCL), and wherein said subset of genes g includes one or more genes selected from the group consisting of genes corresponding to (listed by GENBANK accession number): AK097859.1, NM_(—)001759.2, NM_(—)002827.2, NM_(—)005574.2, NM_(—)004513.4, NM_(—)001080416.1, XM_(—)378655.2, NM_(—)002827.2, NM_(—)001018009.2, AK097859.1, NM_(—)001012505.1, NM_(—)001987.4, BC106050.1, NM_(—)001042518.1, NM_(—)001706.2, NM_(—)001101676.1, NM_(—)002460.1, NM_(—)002648.2, NM_(—)014957.2, NM_(—)013314.2, NM_(—)004480.3, NM_(—)004480.3, NM_(—)001098175.1, NM_(—)006152.2, NM_(—)0143973, NM_(—)002221.2, NM_(—)013314.2, NM_(—)001003940.1, and NM_(—)174908.2.
 28. The method of claim 14, wherein said microarray is selected from the group consisting of an Affymetrix U133A microarray and an Affymetrix U133B microarray.
 29. The method of claim 1, wherein the method further comprises providing a therapy or course of treatment based on whether sample X belongs to said first lymphoma type, said second lymphoma type, or neither said first lymphoma type nor said second lymphoma type. 